The estimation of the electrical model parameters of solar PV, such as light-induced current, diode dark saturation current, thermal voltage, series resistance, and shunt resistance, is indispensable to predict the actual electrical performance of solar photovoltaic (PV) under changing environmental conditions. Therefore, this paper first considers the various methods of parameter estimation of solar PV to highlight their shortfalls. Thereafter, a new parameter estimation method, based on multi-objective optimisation, namely, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is proposed. Furthermore, to check the effectiveness and accuracy of the proposed method, conventional methods, such as, ‘Newton-Raphson’, ‘Particle Swarm Optimisation, Search Algorithm, was tested on four solar PV modules of polycrystalline and monocrystalline materials. Finally, a solar PV module photowatt PWP201 has been considered and compared with six different state of art methods. The estimated performance indices such as current absolute error matrics, absolute relative power error, mean absolute error, and P-V characteristics curve were compared. The results depict the close proximity of the characteristic curve obtained with the proposed NSGA-II method to the curve obtained by the manufacturer’s datasheet.
This document discusses ENEA, the Italian Energy, New Technologies and Environment Agency. ENEA's mission is to support Italy's competitiveness and sustainable development. The document discusses ENEA's focus areas including environment, biotechnology, nuclear energy, new materials, and energy efficiency/renewables. It then discusses using soft computing approaches for modeling ambient temperature and humidity, optimizing eco-building design, and forecasting regional energy consumption in Italy. Neural networks, genetic algorithms, and hybrid models are evaluated for developing accurate models with limited historical data.
The document is a final report for an optimal system operation project. It was authored by Oswaldo Guerra Gomez, a student at Saxion University of Applied Sciences in the Netherlands under the supervision of Mr. Nguyen Trung Thang and Mr. Jan Bollen. The report details the student's research and implementation of the Flower Pollination Algorithm (FPA) to solve the economic load dispatch (ELD) problem of minimizing power generation costs while meeting demand. The student analyzed various optimization algorithms, programmed FPA in MATLAB, and tested it on standard functions and a 6-unit power system, finding it outperformed other algorithms in accuracy and speed. The report also includes a documentary about Vietnam made during the
A Solution to Optimal Power Flow Problem using Artificial Bee Colony Algorith...IOSR Journals
This document presents an artificial bee colony (ABC) algorithm approach to solve the optimal power flow (OPF) problem incorporating a flexible AC transmission system (FACTS) device, specifically a static synchronous series compensator (SSSC). The ABC algorithm is tested on the IEEE 14-bus test system both with and without the SSSC. Results show that the ABC algorithm gives a better solution when incorporating the SSSC, improving the system performance in terms of lower total cost, lower power losses, and better voltage profile compared to the case without SSSC.
This document summarizes a research paper that proposes using a genetic algorithm to optimize the placement of FACTS devices (TCSC and SVC) to maximize available transfer capability (ATC) and minimize contingencies in a power system. It first provides background on ATC and FACTS devices. It then describes modeling TCSC and SVC and constructing the genetic algorithm. The algorithm is tested on a two-area 11 bus power system model. Results show that optimally placing TCSC and SVC using the genetic algorithm can increase ATC and reduce contingencies compared to having no FACTS devices.
Multi-Objective Optimization Based Design of High Efficiency DC-DC Switching ...IJPEDS-IAES
In this paper we explore the feasibility of applying multi objective stochastic
optimization algorithms to the optimal design of switching DC-DC
converters, in this way allowing the direct determination of the Pareto
optimal front of the problem. This approach provides the designer, at
affordable computational cost, a complete optimal set of choices, and a more
general insight in the objectives and parameters space, as compared to other
design procedures. As simple but significant study case we consider a low
power DC-DC hybrid control buck converter. Its optimal design is fully
analyzed basing on a Matlab public domain implementations for the
considered algorithms, the GODLIKE package implementing Genetic
Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated
Annealing (SA). In this way, in a unique optimization environment, three
different optimization approaches are easily implemented and compared.
Basic assumptions for the Matlab model of the converter are briefly
discussed, and the optimal design choice is validated “a-posteriori” with
SPICE simulations.
Optimization Methods and Algorithms for Solving Of Hydro- Thermal Scheduling ...IOSR Journals
This document reviews various optimization methods and algorithms that have been used to solve hydrothermal scheduling problems. It finds that while older methods have limitations, newer methods improve on them by providing more efficiency, faster convergence, robustness and adaptability. Specifically, it discusses optimization techniques like Lagrangian relaxation, augmented Lagrangian methods, differential evolution, genetic algorithms and particle swarm optimization that have been applied to solve the hydrothermal scheduling problem. It also compares the performance of these various algorithms on test problems and finds that methods like enhanced cultural algorithm and fuzzy adaptive particle swarm optimization generate better solutions than other approaches.
Optimal Integration of the Renewable Energy to the Grid by Considering Small ...IJECEIAES
In recent decades, one of the main management’s concerns of professional engineers is the optimal integration of various types of renewable energy to the grid. This paper discusses the optimal allocation of one type of renewable energy i.e. wind turbine to the grid for enhancing network’s performance. A multi-objective function is used as indexes of the system’s performance, such as increasing system loadability and minimizing the loss of real power transmission line by considering security and stability of systems’ constraints viz.: voltage and line margins, and eigenvalues as well which is representing as small signal stability. To solve the optimization problems, a new method has been developed using a novel variant of the Genetic Algorithm (GA), specifically known as Non-dominated Sorting Genetic Algorithm II (NSGAII). Whereas the Fuzzy-based mechanism is used to support the decision makers prefer the best compromise solution from the Pareto front. The effectiveness of the developed method has been established on a modified IEEE 14-bus system with wind turbine system, and their simulation results showed that the dynamic performance of the power system can be effectively improved by considering the stability and security of the system.
GWO-based estimation of input-output parameters of thermal power plantsTELKOMNIKA JOURNAL
This document presents a study that uses the Grey Wolf Optimizer (GWO) method to estimate the input-output parameters of the fuel cost curve for thermal power plants.
The fuel cost curve represents the relationship between a plant's fuel costs and power output, and needs to be periodically re-estimated due to temperature and aging effects. Accurately estimating the curve's parameters is important for economic dispatch calculations.
The study formulates parameter estimation as an optimization problem to minimize errors between actual and estimated fuel costs. It applies GWO to find the parameters for different fuel cost curve models using test data from three power plants. Simulation results show GWO provides better parameter estimates than other estimation methods.
This document discusses ENEA, the Italian Energy, New Technologies and Environment Agency. ENEA's mission is to support Italy's competitiveness and sustainable development. The document discusses ENEA's focus areas including environment, biotechnology, nuclear energy, new materials, and energy efficiency/renewables. It then discusses using soft computing approaches for modeling ambient temperature and humidity, optimizing eco-building design, and forecasting regional energy consumption in Italy. Neural networks, genetic algorithms, and hybrid models are evaluated for developing accurate models with limited historical data.
The document is a final report for an optimal system operation project. It was authored by Oswaldo Guerra Gomez, a student at Saxion University of Applied Sciences in the Netherlands under the supervision of Mr. Nguyen Trung Thang and Mr. Jan Bollen. The report details the student's research and implementation of the Flower Pollination Algorithm (FPA) to solve the economic load dispatch (ELD) problem of minimizing power generation costs while meeting demand. The student analyzed various optimization algorithms, programmed FPA in MATLAB, and tested it on standard functions and a 6-unit power system, finding it outperformed other algorithms in accuracy and speed. The report also includes a documentary about Vietnam made during the
A Solution to Optimal Power Flow Problem using Artificial Bee Colony Algorith...IOSR Journals
This document presents an artificial bee colony (ABC) algorithm approach to solve the optimal power flow (OPF) problem incorporating a flexible AC transmission system (FACTS) device, specifically a static synchronous series compensator (SSSC). The ABC algorithm is tested on the IEEE 14-bus test system both with and without the SSSC. Results show that the ABC algorithm gives a better solution when incorporating the SSSC, improving the system performance in terms of lower total cost, lower power losses, and better voltage profile compared to the case without SSSC.
This document summarizes a research paper that proposes using a genetic algorithm to optimize the placement of FACTS devices (TCSC and SVC) to maximize available transfer capability (ATC) and minimize contingencies in a power system. It first provides background on ATC and FACTS devices. It then describes modeling TCSC and SVC and constructing the genetic algorithm. The algorithm is tested on a two-area 11 bus power system model. Results show that optimally placing TCSC and SVC using the genetic algorithm can increase ATC and reduce contingencies compared to having no FACTS devices.
Multi-Objective Optimization Based Design of High Efficiency DC-DC Switching ...IJPEDS-IAES
In this paper we explore the feasibility of applying multi objective stochastic
optimization algorithms to the optimal design of switching DC-DC
converters, in this way allowing the direct determination of the Pareto
optimal front of the problem. This approach provides the designer, at
affordable computational cost, a complete optimal set of choices, and a more
general insight in the objectives and parameters space, as compared to other
design procedures. As simple but significant study case we consider a low
power DC-DC hybrid control buck converter. Its optimal design is fully
analyzed basing on a Matlab public domain implementations for the
considered algorithms, the GODLIKE package implementing Genetic
Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated
Annealing (SA). In this way, in a unique optimization environment, three
different optimization approaches are easily implemented and compared.
Basic assumptions for the Matlab model of the converter are briefly
discussed, and the optimal design choice is validated “a-posteriori” with
SPICE simulations.
Optimization Methods and Algorithms for Solving Of Hydro- Thermal Scheduling ...IOSR Journals
This document reviews various optimization methods and algorithms that have been used to solve hydrothermal scheduling problems. It finds that while older methods have limitations, newer methods improve on them by providing more efficiency, faster convergence, robustness and adaptability. Specifically, it discusses optimization techniques like Lagrangian relaxation, augmented Lagrangian methods, differential evolution, genetic algorithms and particle swarm optimization that have been applied to solve the hydrothermal scheduling problem. It also compares the performance of these various algorithms on test problems and finds that methods like enhanced cultural algorithm and fuzzy adaptive particle swarm optimization generate better solutions than other approaches.
Optimal Integration of the Renewable Energy to the Grid by Considering Small ...IJECEIAES
In recent decades, one of the main management’s concerns of professional engineers is the optimal integration of various types of renewable energy to the grid. This paper discusses the optimal allocation of one type of renewable energy i.e. wind turbine to the grid for enhancing network’s performance. A multi-objective function is used as indexes of the system’s performance, such as increasing system loadability and minimizing the loss of real power transmission line by considering security and stability of systems’ constraints viz.: voltage and line margins, and eigenvalues as well which is representing as small signal stability. To solve the optimization problems, a new method has been developed using a novel variant of the Genetic Algorithm (GA), specifically known as Non-dominated Sorting Genetic Algorithm II (NSGAII). Whereas the Fuzzy-based mechanism is used to support the decision makers prefer the best compromise solution from the Pareto front. The effectiveness of the developed method has been established on a modified IEEE 14-bus system with wind turbine system, and their simulation results showed that the dynamic performance of the power system can be effectively improved by considering the stability and security of the system.
GWO-based estimation of input-output parameters of thermal power plantsTELKOMNIKA JOURNAL
This document presents a study that uses the Grey Wolf Optimizer (GWO) method to estimate the input-output parameters of the fuel cost curve for thermal power plants.
The fuel cost curve represents the relationship between a plant's fuel costs and power output, and needs to be periodically re-estimated due to temperature and aging effects. Accurately estimating the curve's parameters is important for economic dispatch calculations.
The study formulates parameter estimation as an optimization problem to minimize errors between actual and estimated fuel costs. It applies GWO to find the parameters for different fuel cost curve models using test data from three power plants. Simulation results show GWO provides better parameter estimates than other estimation methods.
Compromising between-eld-&-eed-using-gatool-matlabSubhankar Sau
Creating a compromising points between economic load dispatch & emission created from the plant to minimising those effects.
these are created by using MATLAB and GATOOL .
taking Weighted Sum Method,also Pareto optimal curve.
created by: SUBHANKAR SAU
The optimal solution for unit commitment problem using binary hybrid grey wol...IJECEIAES
The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
Optimal Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
Multi-objective based economic environmental dispatch with stochastic solar-w...IJECEIAES
This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.
Resource aware wind farm and D-STATCOM optimal sizing and placement in a dist...IJECEIAES
Doubly fed induction generators (DFIG) based wind farms are capable of providing reactive power compensation. Compensation capability enhancement using reactors such as distributed static synchronous compensator (D-STATCOM) while connecting distribution generation (DG) systems to grid is imperative. This paper presents an optimal placement and sizing of offshore wind farms in a coastal distribution system that is emulated on an IEEE 33 bus system. A multi-objective formulation for optimal placement and sizing of the offshore wind farms with both the location and size constraints is developed. Teaching learning algorithm is used to optimize the multi-objective function constraining on the capacity and location of the offshore wind farms. The proposed formulation is a multi-objective problem for placement of the wind generator in the power system with dynamic wind supply to the power system. The random wind speed is generated as the input and the wind farm output generated to perform the optimal sizing and placement in the distributed system. MATLAB based simulation developed is found to be efficient and robust.
Electronics system thermal management optimization using finite element and N...TELKOMNIKA JOURNAL
This document summarizes the optimization of electronics system thermal management using finite element analysis and the Nelder-Mead optimization method. A test sample with 3 components was modeled and optimized to minimize average temperature and area of the printed circuit board (PCB). The results showed temperature reductions of up to 4-8% and a 34% reduction in PCB area. Comparative analyses were also performed on samples from other studies, achieving temperature reductions of up to 23% and area reductions of up to 26%. Overall, the optimization approach significantly improved thermal management and performance while reducing PCB size.
This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.
IRJET- A Review on Computational Determination of Global Maximum Power Point ...IRJET Journal
This document reviews computational techniques for determining the global maximum power point (GMPP) for photovoltaic (PV) arrays under partial shading conditions. Partial shading causes the PV array characteristics to exhibit multiple local maxima, making it difficult for conventional maximum power point tracking techniques to identify the true GMPP. The document categorizes and discusses analytical, meta-heuristic, and fuzzy-based computational approaches that have been proposed to address this issue, including methods using the Lambert W function, particle swarm optimization, simulated annealing, and fuzzy logic. It proposes using the Das-Saetre model of PV characteristics along with meta-heuristic techniques to more accurately compute the GMPP while reducing computational complexity compared to previous approaches.
Hybrid bypass technique to mitigate leakage current in the grid-tied inverterIJECEIAES
The extensive use of fossil fuel is destroying the balance of nature that could lead to many problems in the forthcoming era. Renewable energy resources are a ray of hope to avoid possible destruction. Smart grid and distributed power generation systems are now mainly built with the help of renewable energy resources. The integration of renewable energy production system with the smart grid and distributed power generation is facing many challenges that include addressing the issue of isolation and power quality. This paper presents a new approach to address the aforementioned issues by proposing a hybrid bypass technique concept to improve the overall performance of the grid-tied inverter in solar power generation. The topology with the proposed technique is presented using traditional H5, oH5 and H6 inverter. Comparison of topologies with literature is carried out to check the feasibility of the method proposed. It is found that the leakage current of all the proposed inverters is 9 mA and total harmonic distortion is almost about 2%. The proposed topology has good efficiency, common mode and differential mode characteristics.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
A hybrid algorithm for voltage stability enhancement of distribution systems IJECEIAES
This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.
IRJET- A New Approach to Economic Load Dispatch by using Improved QEMA ba...IRJET Journal
This document proposes an improved Quantum behaved electro-magnetism algorithm particle swarm optimization (QEMAPSO) approach to solve the economic load dispatch (ELD) problem. The objective is to minimize the total generation cost while considering constraints like generator limits, transmission losses, and valve point effects. It formulates the ELD problem and describes the QEMA-PSO algorithm which uses QEMA to determine an initial solution that is then optimized using PSO. Results on a 6-generator IEEE 30-bus test system show that the proposed QEMA-PSO approach improves upon other methods like genetic algorithm and dance bee colony optimization in minimizing costs and emissions.
Benchmarking study between capacitive and electronic load technic to track I-...IJECEIAES
To detect defects of solar panel and understand the effect of external parameters such as fluctuations in illumination, temperature, and the effect of a type of dust on a photovoltaic (PV) panel, it is essential to plot the Ipv=f(Vpv) characteristic of the PV panel, and the simplest way to plot this I-V characteristic is to use a variable resistor. This paper presents a study of comparison and combination between two methods: capacitive and electronic loading to track I-V characteristic. The comparison was performed in terms of accuracy, response time and instrumentation cost used in each circuit, under standard temperature and illumination conditions by using polycrystalline solar panel type SX330J and monocrystalline solar panels type ET-M53630. The whole system is based on simple components, less expensive and especially widely used in laboratories. The results will be between the datasheet of the manufacturer with the experimental data, refinements and improvements concerning the number of points and the trace time have been made by combining these two methods.
Normally, the character of the wind energy as a renewable energy sources has uncertainty in generation. To resolve the Optimal Power Flow (OPF) drawback, this paper proposed a replacement Hybrid Multi Objective Artificial Physical Optimization (HMOAPO) algorithmic rule, which does not require any management parameters compared to different meta-heuristic algorithms within the literature. Artificial Physical Optimization (APO), a moderately new population-based intelligence algorithm, shows fine performance on improvement issues. Moreover, this paper presents hybrid variety of Animal Migration Optimization (AMO) algorithmic rule to express the convergence characteristic of APO. The OPF drawback is taken into account with six totally different check cases, the effectiveness of the proposed HMOAPO technique is tested on IEEE 30-bus, IEEE 118-bus and IEEE 300-bus check system. The obtained results from the HMOAPO algorithm is compared with the other improvement techniques within the literature. The obtained comparison results indicate that proposed technique is effective to succeed in best resolution for the OPF drawback.
Modeling and Parameter Extraction of PV Modules Using Genetic Algorithms and ...IOSR Journals
This document presents a technique using genetic algorithms and differential evaluation to improve the accuracy of extracting solar cell parameters from current-voltage data. A two-diode solar cell model with seven parameters is used. Genetic algorithms and differential evaluation are formulated as search and optimization problems to determine the model parameters. Results show that when simulated parameter errors are ±5%, the extracted parameters have deviations of 0.1-1% of the specified values, demonstrating improved accuracy over conventional extraction techniques. The document also describes developing a photovoltaic module model in MATLAB/Simulink and using it to simulate current-voltage and power-voltage curves under varying light intensity and temperature conditions.
VOLTAGE PROFILE IMPROVEMENT AND LINE LOSSES REDUCTION USING DG USING GSA AND ...Journal For Research
In recent years, the power industry has experienced significant changes on the power distribution systems primarily due to the implementation of smart-grid technology and the incremental implementation of distributed generation. Distributed Generation (DG) is simply defined as the decentralization of power plants by placing smaller generating units closer to the point of consumption, traditionally ten mega-watts or smaller. The distribution power system is generally designed for radial power flow, but with the introduction of DG, power flow becomes bidirectional. Therefore this thesis focuses on testing various indices and using effective techniques for the optimal placement and sizing of the DG unit by minimizing power losses and voltage deviation. A 14-bus radial distribution system has been taken as the test system. The feasibility of the work lies on the fast execution of the programs as it would be equipped with the real time operation of the distribution system and it is seen that execution of the DG placement is quite fast and feasible with the optimization techniques used in this work.
Modified T-type topology of three-phase multi-level inverter for photovoltaic...IJECEIAES
In this article, a three-phase multilevel neutral-point-clamped inverter with a modified t-type structure of switches is proposed. A pulse width modulation (PWM) scheme of the proposed inverter is also developed. The proposed topology of the multilevel inverter has the advantage of being simple, on the one hand since it does contain only semiconductors in reduced number (corresponding to the number of required voltage levels), and no other components such as switching or flying capacitors, and on the other hand, the control scheme is much simpler and more suitable for variable frequency and voltage control. The performances of this inverter are analyzed through simulations carried out in the MATLAB/Simulink environment on a threephase inverter with 9 levels. In all simulations, the proposed topology is connected with R-load or RL-load without any output filter.
Maximum power point tracking techniques for photovoltaic systems: a comparati...IJECEIAES
Photovoltaic (PV) systems are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.
Small Signal Stability Improvement and Congestion Management Using PSO Based ...IDES Editor
In this paper an attempt has been made to study the
application of Thyristor Controlled Series Capacitor (TCSC)
to mitigate small signal stability problem in addition to
congestion management of a heavily loaded line in a
multimachine power system. The Flexible AC Transmission
System (FACTS) devices such as TCSC can be used to control
the power flows in the network and can help in improvement
of small signal stability aspect. It can also provide relief to
congestion in the heavily loaded line. However, the
performance of any FACTS device highly depends upon its
parameters and placement at suitable locations in the power
network. In this paper, Particle Swarm Optimization (PSO)
method has been used for determining the optimal locations
and parameters of the TCSC controller in order to damp small
signal oscillations. Transmission Line Flow (TLF) Sensitivity
method has been used for curtailment of non-firm load to
limit power flow congestion. The results of simulation reveals
that TCSC controllers, placed optimally, not only mitigate
small signal oscillations but they can also alleviate line flow
congestion effectively.
Power loss reduction, improvement of voltage profile, system reliability and system security are the important objectives that motivated researchers to use custom power devices/FACTS devices in power systems. The existing power quality problems such as power losses, voltage instability, voltage profile problem, load ability issues, energy losses, reliability problems etc. are caused due to continuous load growth and outage of components. The significant qualities of custom power devices /FACTS devices such as power loss reduction, improvement of voltage profile, system reliability and system security have motivated researchers in this area and to implement these devices in power system. The optimal placement and sizing of these devices are determined based on economical viability, required quality, reliability and availability. In published literatures, different algorithms are implemented for optimal placement of these devices based on different conditions. In this paper, the published literatures on this field are comprehensively reviewed and elaborate comparison of various algorithms is compared. The inference of this extensive comparative analysis is presented. In this research, Meta heuristic methods and sensitive index methods are used for determining the optimal location and sizing of custom power devices/FACTS devices. The combination of these two methods are also implemented and presented.
This document presents a cuckoo search algorithm for tracking the global maximum power point of a photovoltaic system under partial shading conditions. The cuckoo search algorithm is able to accurately track the global maximum power point with high efficiency in less time under different conditions including partial shading. Its performance is compared to the perturb and observe algorithm, showing that cuckoo search performs better by avoiding getting trapped at local maxima under partial shading, improving reliability and reducing losses for the power system. The cuckoo search algorithm controls the duty cycle of the DC-DC converter directly without a PID controller.
Improvement of grid connected photovoltaic system using artificial neural net...ijscmcj
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.
Compromising between-eld-&-eed-using-gatool-matlabSubhankar Sau
Creating a compromising points between economic load dispatch & emission created from the plant to minimising those effects.
these are created by using MATLAB and GATOOL .
taking Weighted Sum Method,also Pareto optimal curve.
created by: SUBHANKAR SAU
The optimal solution for unit commitment problem using binary hybrid grey wol...IJECEIAES
The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
Optimal Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
Multi-objective based economic environmental dispatch with stochastic solar-w...IJECEIAES
This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.
Resource aware wind farm and D-STATCOM optimal sizing and placement in a dist...IJECEIAES
Doubly fed induction generators (DFIG) based wind farms are capable of providing reactive power compensation. Compensation capability enhancement using reactors such as distributed static synchronous compensator (D-STATCOM) while connecting distribution generation (DG) systems to grid is imperative. This paper presents an optimal placement and sizing of offshore wind farms in a coastal distribution system that is emulated on an IEEE 33 bus system. A multi-objective formulation for optimal placement and sizing of the offshore wind farms with both the location and size constraints is developed. Teaching learning algorithm is used to optimize the multi-objective function constraining on the capacity and location of the offshore wind farms. The proposed formulation is a multi-objective problem for placement of the wind generator in the power system with dynamic wind supply to the power system. The random wind speed is generated as the input and the wind farm output generated to perform the optimal sizing and placement in the distributed system. MATLAB based simulation developed is found to be efficient and robust.
Electronics system thermal management optimization using finite element and N...TELKOMNIKA JOURNAL
This document summarizes the optimization of electronics system thermal management using finite element analysis and the Nelder-Mead optimization method. A test sample with 3 components was modeled and optimized to minimize average temperature and area of the printed circuit board (PCB). The results showed temperature reductions of up to 4-8% and a 34% reduction in PCB area. Comparative analyses were also performed on samples from other studies, achieving temperature reductions of up to 23% and area reductions of up to 26%. Overall, the optimization approach significantly improved thermal management and performance while reducing PCB size.
This paper focuses on the artificial bee colony (ABC) algorithm, which is a nonlinear optimization problem. is proposed to find the optimal power flow (OPF). To solve this problem, we will apply the ABC algorithm to a power system incorporating wind power. The proposed approach is applied on a standard IEEE-30 system with wind farms located on different buses and with different penetration levels to show the impact of wind farms on the system in order to obtain the optimal settings of control variables of the OPF problem. Based on technical results obtained, the ABC algorithm is shown to achieve a lower cost and losses than the other methods applied, while incorporating wind power into the system, high performance would be gained.
IRJET- A Review on Computational Determination of Global Maximum Power Point ...IRJET Journal
This document reviews computational techniques for determining the global maximum power point (GMPP) for photovoltaic (PV) arrays under partial shading conditions. Partial shading causes the PV array characteristics to exhibit multiple local maxima, making it difficult for conventional maximum power point tracking techniques to identify the true GMPP. The document categorizes and discusses analytical, meta-heuristic, and fuzzy-based computational approaches that have been proposed to address this issue, including methods using the Lambert W function, particle swarm optimization, simulated annealing, and fuzzy logic. It proposes using the Das-Saetre model of PV characteristics along with meta-heuristic techniques to more accurately compute the GMPP while reducing computational complexity compared to previous approaches.
Hybrid bypass technique to mitigate leakage current in the grid-tied inverterIJECEIAES
The extensive use of fossil fuel is destroying the balance of nature that could lead to many problems in the forthcoming era. Renewable energy resources are a ray of hope to avoid possible destruction. Smart grid and distributed power generation systems are now mainly built with the help of renewable energy resources. The integration of renewable energy production system with the smart grid and distributed power generation is facing many challenges that include addressing the issue of isolation and power quality. This paper presents a new approach to address the aforementioned issues by proposing a hybrid bypass technique concept to improve the overall performance of the grid-tied inverter in solar power generation. The topology with the proposed technique is presented using traditional H5, oH5 and H6 inverter. Comparison of topologies with literature is carried out to check the feasibility of the method proposed. It is found that the leakage current of all the proposed inverters is 9 mA and total harmonic distortion is almost about 2%. The proposed topology has good efficiency, common mode and differential mode characteristics.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
A hybrid algorithm for voltage stability enhancement of distribution systems IJECEIAES
This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.
IRJET- A New Approach to Economic Load Dispatch by using Improved QEMA ba...IRJET Journal
This document proposes an improved Quantum behaved electro-magnetism algorithm particle swarm optimization (QEMAPSO) approach to solve the economic load dispatch (ELD) problem. The objective is to minimize the total generation cost while considering constraints like generator limits, transmission losses, and valve point effects. It formulates the ELD problem and describes the QEMA-PSO algorithm which uses QEMA to determine an initial solution that is then optimized using PSO. Results on a 6-generator IEEE 30-bus test system show that the proposed QEMA-PSO approach improves upon other methods like genetic algorithm and dance bee colony optimization in minimizing costs and emissions.
Benchmarking study between capacitive and electronic load technic to track I-...IJECEIAES
To detect defects of solar panel and understand the effect of external parameters such as fluctuations in illumination, temperature, and the effect of a type of dust on a photovoltaic (PV) panel, it is essential to plot the Ipv=f(Vpv) characteristic of the PV panel, and the simplest way to plot this I-V characteristic is to use a variable resistor. This paper presents a study of comparison and combination between two methods: capacitive and electronic loading to track I-V characteristic. The comparison was performed in terms of accuracy, response time and instrumentation cost used in each circuit, under standard temperature and illumination conditions by using polycrystalline solar panel type SX330J and monocrystalline solar panels type ET-M53630. The whole system is based on simple components, less expensive and especially widely used in laboratories. The results will be between the datasheet of the manufacturer with the experimental data, refinements and improvements concerning the number of points and the trace time have been made by combining these two methods.
Normally, the character of the wind energy as a renewable energy sources has uncertainty in generation. To resolve the Optimal Power Flow (OPF) drawback, this paper proposed a replacement Hybrid Multi Objective Artificial Physical Optimization (HMOAPO) algorithmic rule, which does not require any management parameters compared to different meta-heuristic algorithms within the literature. Artificial Physical Optimization (APO), a moderately new population-based intelligence algorithm, shows fine performance on improvement issues. Moreover, this paper presents hybrid variety of Animal Migration Optimization (AMO) algorithmic rule to express the convergence characteristic of APO. The OPF drawback is taken into account with six totally different check cases, the effectiveness of the proposed HMOAPO technique is tested on IEEE 30-bus, IEEE 118-bus and IEEE 300-bus check system. The obtained results from the HMOAPO algorithm is compared with the other improvement techniques within the literature. The obtained comparison results indicate that proposed technique is effective to succeed in best resolution for the OPF drawback.
Modeling and Parameter Extraction of PV Modules Using Genetic Algorithms and ...IOSR Journals
This document presents a technique using genetic algorithms and differential evaluation to improve the accuracy of extracting solar cell parameters from current-voltage data. A two-diode solar cell model with seven parameters is used. Genetic algorithms and differential evaluation are formulated as search and optimization problems to determine the model parameters. Results show that when simulated parameter errors are ±5%, the extracted parameters have deviations of 0.1-1% of the specified values, demonstrating improved accuracy over conventional extraction techniques. The document also describes developing a photovoltaic module model in MATLAB/Simulink and using it to simulate current-voltage and power-voltage curves under varying light intensity and temperature conditions.
VOLTAGE PROFILE IMPROVEMENT AND LINE LOSSES REDUCTION USING DG USING GSA AND ...Journal For Research
In recent years, the power industry has experienced significant changes on the power distribution systems primarily due to the implementation of smart-grid technology and the incremental implementation of distributed generation. Distributed Generation (DG) is simply defined as the decentralization of power plants by placing smaller generating units closer to the point of consumption, traditionally ten mega-watts or smaller. The distribution power system is generally designed for radial power flow, but with the introduction of DG, power flow becomes bidirectional. Therefore this thesis focuses on testing various indices and using effective techniques for the optimal placement and sizing of the DG unit by minimizing power losses and voltage deviation. A 14-bus radial distribution system has been taken as the test system. The feasibility of the work lies on the fast execution of the programs as it would be equipped with the real time operation of the distribution system and it is seen that execution of the DG placement is quite fast and feasible with the optimization techniques used in this work.
Modified T-type topology of three-phase multi-level inverter for photovoltaic...IJECEIAES
In this article, a three-phase multilevel neutral-point-clamped inverter with a modified t-type structure of switches is proposed. A pulse width modulation (PWM) scheme of the proposed inverter is also developed. The proposed topology of the multilevel inverter has the advantage of being simple, on the one hand since it does contain only semiconductors in reduced number (corresponding to the number of required voltage levels), and no other components such as switching or flying capacitors, and on the other hand, the control scheme is much simpler and more suitable for variable frequency and voltage control. The performances of this inverter are analyzed through simulations carried out in the MATLAB/Simulink environment on a threephase inverter with 9 levels. In all simulations, the proposed topology is connected with R-load or RL-load without any output filter.
Maximum power point tracking techniques for photovoltaic systems: a comparati...IJECEIAES
Photovoltaic (PV) systems are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria’s: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time.
Small Signal Stability Improvement and Congestion Management Using PSO Based ...IDES Editor
In this paper an attempt has been made to study the
application of Thyristor Controlled Series Capacitor (TCSC)
to mitigate small signal stability problem in addition to
congestion management of a heavily loaded line in a
multimachine power system. The Flexible AC Transmission
System (FACTS) devices such as TCSC can be used to control
the power flows in the network and can help in improvement
of small signal stability aspect. It can also provide relief to
congestion in the heavily loaded line. However, the
performance of any FACTS device highly depends upon its
parameters and placement at suitable locations in the power
network. In this paper, Particle Swarm Optimization (PSO)
method has been used for determining the optimal locations
and parameters of the TCSC controller in order to damp small
signal oscillations. Transmission Line Flow (TLF) Sensitivity
method has been used for curtailment of non-firm load to
limit power flow congestion. The results of simulation reveals
that TCSC controllers, placed optimally, not only mitigate
small signal oscillations but they can also alleviate line flow
congestion effectively.
Power loss reduction, improvement of voltage profile, system reliability and system security are the important objectives that motivated researchers to use custom power devices/FACTS devices in power systems. The existing power quality problems such as power losses, voltage instability, voltage profile problem, load ability issues, energy losses, reliability problems etc. are caused due to continuous load growth and outage of components. The significant qualities of custom power devices /FACTS devices such as power loss reduction, improvement of voltage profile, system reliability and system security have motivated researchers in this area and to implement these devices in power system. The optimal placement and sizing of these devices are determined based on economical viability, required quality, reliability and availability. In published literatures, different algorithms are implemented for optimal placement of these devices based on different conditions. In this paper, the published literatures on this field are comprehensively reviewed and elaborate comparison of various algorithms is compared. The inference of this extensive comparative analysis is presented. In this research, Meta heuristic methods and sensitive index methods are used for determining the optimal location and sizing of custom power devices/FACTS devices. The combination of these two methods are also implemented and presented.
This document presents a cuckoo search algorithm for tracking the global maximum power point of a photovoltaic system under partial shading conditions. The cuckoo search algorithm is able to accurately track the global maximum power point with high efficiency in less time under different conditions including partial shading. Its performance is compared to the perturb and observe algorithm, showing that cuckoo search performs better by avoiding getting trapped at local maxima under partial shading, improving reliability and reducing losses for the power system. The cuckoo search algorithm controls the duty cycle of the DC-DC converter directly without a PID controller.
Improvement of grid connected photovoltaic system using artificial neural net...ijscmcj
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.
This document presents a study that uses artificial neural networks (ANN) and genetic algorithms (GA) to improve the maximum power point tracking (MPPT) of a grid-connected photovoltaic system under different operating conditions. GA is used to optimize the data and determine the optimal voltage corresponding to the maximum power point. This optimized data is then used to train the ANN. The trained ANN is able to track the maximum power point with fewer fluctuations compared to conventional MPPT methods. A grid side p-q controller is also implemented to control both the line voltage and current and allow active and reactive power exchange with the grid. Simulation results in Matlab/Simulink demonstrate the effectiveness of the ANN-GA controller
In this paper two configurations of solar photovoltaic energy conversion using the NPC five levels inverter for stand-alone application in south Algeria are proposed and their performances compared. The first cascade uses four separate PV sources and the second configuration use only one PV generator. In these two cases and without DC/DC converter introduced between PV source and inverter and to get a stable AC voltage, authors in propose a proportional regulator of inverter modulation index. The SVPWM technique is used in order to get the best voltage waveform. For the second configuration proposed, we introduce in the control loops another algorithm which uses the redundant vectors of space vector diagram of inverter to stabilise the DC bus voltages. A real data of temperature and solar irradiation obtained by radiometric station in Ghardaïa city in south Algeria are used to test the performance of proposed controls. The simulation results show that the inverter output voltage is stable for the two configurations proposed despite the variation of solar irradiation, temperature and load. Also, the THD obtained is in the limits of international standards. Then, the PV cascade with separate PV sources is the best solution, seeing that we do not need to use another algorithm in the control loops.
IRJET- Implementation of Conventional Perturb with different Load for Maximum...IRJET Journal
This document discusses the implementation of a modified perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for a photovoltaic system using a microcontroller. The traditional P&O algorithm is simple but has issues during rapid changes in irradiance/load, including oscillating around the MPP or moving away from it. The proposed algorithm adds a constant load method to help the traditional P&O algorithm identify the cause of power changes and make better decisions during initial perturbations. Simulation and experimental results show the proposed algorithm performs better than the traditional P&O approach.
Estimation of Photovoltaic Module Parameters based on Total Error Minimizatio...journalBEEI
Mathematical Modelling of photovoltaic (PV) modules is important for simulation and performance analysis of PV system. Therefore, an accurate parameters estimation is necessary. Single-diode and two-diode model are widely used to model the PV system. However, it required to determine several parameters such as series and shunt resistances that not provided in datasheet. The main goal of PV modelling technique is to obtain the accurate parameters to ensure the I-V characteristic is closed to the manufacturer datasheet. Previously, the maximum power error of calculated and datasheet value are considered as objective to be minimized for both models. This paper proposes the PV parameter estimation model based minimizing the total error of open circuit voltage (VOC), short circuit current (ISC) and maximum power (PMAX) where all these parameters are provided by the manufacturer. The performance of single-diode and two-diode models are tested on different type of PV modules using MATLAB. It found that the two-diode model obtained accurate parameters with smaller error compared to single-diode model. However, the simulation time is slightly higher than single-diode model due extra calculation required.
IRJET - Modeling and Simulation of Fuzzy Logic based Controller with Proposed...IRJET Journal
This document presents a study on modeling and simulation of a fuzzy logic based controller with a proposed DC-DC converter for a photovoltaic module. The study aims to increase tracking efficiency and solve drawbacks of conventional maximum power point tracking algorithms. A Cuk converter is proposed and its performance is compared to a boost converter. An optimized fuzzy logic controller is designed using Mamdani fuzzy inference. Simulation results show that the Cuk converter produces higher output voltages and better efficiency than the boost converter under changing irradiance and temperature conditions.
This document describes a study that uses artificial neural networks (ANN) and genetic algorithms (GA) to model and control a grid-connected photovoltaic (PV) system. 390 sets of temperature and irradiance data were optimized using GA to obtain corresponding maximum power point (MPP) voltages. These optimized data were then used to train an ANN for MPPT control. Simulation results in Matlab/Simulink showed that the ANN-GA controller had less fluctuation around the MPP and faster convergence than conventional methods. A P-Q controller was also used to control grid voltage/current and allow both active and reactive power exchange.
This paper demonstrates a mathematical representation of Photovoltaic (PV) solar cells and hence panels performance. One-diode solar cell model is implemented to simulate the cell and extract the performance indications. The tested PV modules are BP Solar (60 Watt) and Synthesis Power (50 Watts), which are operating in a PV generation system in the University of Anbar - Iraq, College of Applied Sciences. The math model demonstrates Power versus Voltage (P-V) characteristic curves to depict and study various parameters with affecting variations in the PV array performance. The parameters include ambient and cell temperature degrees and solar irradiance (G) level which are the main elements to dictate the productivity of a solar system. G is represented by sun unit (1 sun=1 kW/m2). The outcomes of the simulation model characteristics curves have been compared with curves provided by the tested modules data sheets. MATLAB software has been used to simulate the model and extract the results. This paper also investigated photovoltaic simulation with maximum power point tracking (MPPT) converter to evaluate hence predict the behaviors of the whole photovoltaic DC current generation using PSIM Power Electronics program. The model focuses on the basic components in PV systems; The panel and the DC-DC converter. The modeling outcome data will be used as a reference verifying the performance of the tested modules during the year seasons under the dominating dusty hot weather in western Iraq.
Valuation and Determination of Seven and Five Parameters of Photovoltaic Gene...Ah Roueiha
The mathematical modeling of solar cells is essential for any optimization operation of the efficiency or the diagnosis of photovoltaic generator. The photovoltaic module is generally represented by an equivalent circuit whose parameters are experimentally calculated by using the characteristic current-tension, I-V. The precise determination of these parameters stays a challenge for the researchers, making to a big difference in the models and the digital methods dedicated to their characterizations. In the present paper, We are interested to characterize the parameters of single diode and two diodes models, in order to plan the behavior of the photovoltaic generator under real functioning conditions. We developed an identification method of the parameters using Newton Raphson method by using the software Matlab/Simulink. This method is the faster technique which allows the identification of several parameters and can be used in real time applications. The results of the proposed method show an accordance with the experimental and simulated characteristics of photovoltaic generator.
Extraction of photovoltaic generator parameters through combination of an an...IJECEIAES
In the present work, we propose an improved method based on a combination of an analytical and iterative approach to extract the photovoltaic (PV) module parameters using the measured current-voltage characteristics and the simple diode model. First, we calculate the series resistance using a set of analytical formulas for the base values of the three current-voltage curves. Then, the three other parameters are analytically expressed as functions of serial resistance and ideality factor based on the linear least-squares method. Finally, the ideality factor is calculated applying an iterative algorithm to minimize the normalized root mean square error (NRMSE) value. The proposed method was validated with a real experimental set of two PV generators, which showed the best fit to the I-V curve. Moreover, the proposed method needs only the initial value of the ideality factor.
Multi-objective whale optimization based minimization of loss, maximization o...IJECEIAES
Huge need in electricity causes placement of Distribution Generation (DG)s like Photovoltaics (PV) systems in distribution side for enhancing the loadability by improving the voltage stability and minimization of loss with minimum cost. Many optimal placements of DG have done in focus of minimum loss and improving voltage profile. This Whale optimization is a new optimization technique framed with mathematics of spiral bubble-net feeding behavior of humpback whales for solving a power system multi-objective problem considering cost of the power tariff and DG. Here main objectives are minimizing loss and cost with maximization of voltage stability index. IEEE 69 power system data is used for solution of the proposed method.
This document describes a hybrid maximum power point tracking (MPPT) algorithm for photovoltaic systems. It begins by discussing existing MPPT methods like fractional open circuit voltage, perturb and observe, and incremental MPPT with variable step size. It then proposes a new hybrid algorithm that combines advantages of these methods. The hybrid algorithm initializes the PV array voltage to 78% of open circuit voltage. It then uses perturb and observe with a variable step size and selects between two tracking schemes based on how rapidly irradiation is changing. Simulation results in MATLAB and PSIM showed improved starting performance and tracking effects over earlier MPPT methods.
Improving efficiency of Photovoltaic System with Neural Network Based MPPT Co...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Modeling and Analysis of a Maximum Power Point Tracking Control for Double St...IRJET Journal
This document presents a mathematical model and analysis of a maximum power point tracking (MPPT) control scheme for a double-stage solar photovoltaic system connected to the power grid. It derives the mathematical model for a boost converter used to increase the voltage from the solar panels and sizes the inductor and capacitor values. It then introduces, models, and compares three MPPT techniques - perturb and observe, incremental conductance, and fuzzy logic control - using MATLAB/Simulink software to optimize the efficiency of extracting power from the solar panels. The goal is to track the maximum power point without oscillations and properly design the passive components of the boost converter to operate in continuous conduction mode and extract maximum power over a range of weather conditions
To increase energy yield from an installed photovoltaic (PV) array, particularly during partial shading condition (PSC), a new technique based on reconfigurable PV array interconnection is proposed in this work. The proposed technique works by dynamically changing the interconnection of PV modules to form a new configuration using a switching matrix inside the array. The criteria of good reconfigurable PV array interconnection techniques depend on the efficiency and accuracy of the control algorithm to optimally reconfigure the PV array to maximize the total output power. Hence, this paper proposes a new control algorithm using differential evolution (DE) for photovoltaic array reconfiguration (PVAR). To verify the superiority of the proposed algorithm, DE is compared with the particle swarm optimization (PSO) algorithm. Results confirm that DE performs well in terms of the amount of energy production during PSC. For all the nine shading patterns tested on a 3 × 3 PV array, DE yields 1% to 5% more power than PSO.
Parameter Extraction of PV Module using NLS Algorithm with Experimental Valid...IJECEIAES
Photovoltaic (PV) module parameters act an important task in PV system design and simulation. Most popularly used single diode Rsh model has five unknown electrical parameters such as series resistance (Rse), shunt resistance (Rsh), diode quality factor (a), photo-generated current (Ipg) and dark saturation current (Is) in the mathematical model of PV module. The PV module output voltage and current relationship is represented by a transcendental equation and is not possible to solve analytically. This paper proposes nonlinear least square (NLS) technique to extract five unknown parameters. The proposed technique is compared with other two popular techniques available in the literature such as Villalva’s comprehensive technique and modified Newton-Raphson (N-R) technique. Only two parameters Rse and Rsh are estimated by Villalva’s technique, but all single diode unknown electrical parameters can be estimated by the NLS technique. The accuracy of different estimation techniques is compared in terms of absolute percentage errors at MPP and is found the minimum for the proposed technique. The elapsed time for parameter estimation for NLS technique is minimum and much less compared to other two techniques. Extracted parameters of polycrystalline ELDORA-40 PV panel by the proposed technique have been validated through simulation and experimental current-voltage (I-V) and power-voltage (P-V) characteristics.
MODELING AND SIMULATION OF SOLAR PHOTOVOLTAIC APPLICATION BASED MULTILEVEL IN...ecij
As the solar market is blooming and forecasted to continue this trend in the coming years. The efficiency and reliability of PV based system has always been a contention among researchers. Therefore, multilevel inverters are gaining more assiduity as it has multitude of benefits. It offers high power capability along with low output harmonics. The main disadvantage of MLI is its complexity and requirement of large
number of power devices and passive components. This paper presents a topology that achieves 37.5% reduction in number of passive components and power devices for five-level inverter. This topology is basically based on H-bridge with bi-directional auxiliary switch. This paper includes a stand-alone PV
system in which designing and simulation of Boost converter connected with multilevel inverter for ac load is presented. Perturb and observe MPPT algorithm has been implemented to extract maximum power. The premier objective is to obtain Voltage with less harmonic distortion economically. Multicarrier Sinusoidal PWM techniques have been implemented and analysed for modulation scheme. The Proposed system is
simulated n MATLAB/Simulink platform.
MODELING AND SIMULATION OF SOLAR PHOTOVOLTAIC APPLICATION BASED MULTILEVEL IN...ecij
As the solar market is blooming and forecasted to continue this trend in the coming years. The efficiency and reliability of PV based system has always been a contention among researchers. Therefore, multilevel inverters are gaining more assiduity as it has multitude of benefits. It offers high power capability along with low output harmonics. The main disadvantage of MLI is its complexity and requirement of large
number of power devices and passive components. This paper presents a topology that achieves 37.5% reduction in number of passive components and power devices for five-level inverter. This topology is basically based on H-bridge with bi-directional auxiliary switch. This paper includes a stand-alone PV system in which designing and simulation of Boost converter connected with multilevel inverter for ac load is presented. Perturb and observe MPPT algorithm has been implemented to extract maximum power. The premier objective is to obtain Voltage with less harmonic distortion economically. Multicarrier Sinusoidal
PWM techniques have been implemented and analysed for modulation scheme. The Proposed system is simulated n MATLAB/Simulink platform.
SIMULATION AND ANALYSIS OF DIFFERENT MPPT ALGORITHMS FOR PV SYSTEMIAEME Publication
This document summarizes and compares different maximum power point tracking (MPPT) algorithms that can be used for photovoltaic systems. It begins with an introduction to MPPT and its importance for improving PV system performance. It then describes modeling of the PV system and components like the DC-DC boost converter. Four MPPT algorithms are discussed - Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Controller (FLC), and Particle Swarm Optimization (PSO). The document provides flowcharts to illustrate how each algorithm works and compares their advantages/disadvantages. Simulation results are presented to demonstrate the ability of these algorithms to track the maximum power point under changing irradiance
Similar to Solar PV parameter estimation using multi-objective optimisation (20)
Square transposition: an approach to the transposition process in block cipherjournalBEEI
The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
The document proposes using a particle swarm optimization (PSO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN) for image classification. The PSO algorithm is used to find optimal values for CNN hyperparameters like the number and size of convolutional filters. In experiments on the MNIST handwritten digit dataset, the optimized CNN achieved a testing error rate of 0.87%, which is competitive with state-of-the-art models. The proposed approach finds optimized CNN architectures automatically without requiring manual design or encoding strategies during training.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
A secure and energy saving protocol for wireless sensor networksjournalBEEI
The research domain for wireless sensor networks (WSN) has been extensively conducted due to innovative technologies and research directions that have come up addressing the usability of WSN under various schemes. This domain permits dependable tracking of a diversity of environments for both military and civil applications. The key management mechanism is a primary protocol for keeping the privacy and confidentiality of the data transmitted among different sensor nodes in WSNs. Since node's size is small; they are intrinsically limited by inadequate resources such as battery life-time and memory capacity. The proposed secure and energy saving protocol (SESP) for wireless sensor networks) has a significant impact on the overall network life-time and energy dissipation. To encrypt sent messsages, the SESP uses the public-key cryptography’s concept. It depends on sensor nodes' identities (IDs) to prevent the messages repeated; making security goals- authentication, confidentiality, integrity, availability, and freshness to be achieved. Finally, simulation results show that the proposed approach produced better energy consumption and network life-time compared to LEACH protocol; sensors are dead after 900 rounds in the proposed SESP protocol. While, in the low-energy adaptive clustering hierarchy (LEACH) scheme, the sensors are dead after 750 rounds.
Plant leaf identification system using convolutional neural networkjournalBEEI
This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.
Customized moodle-based learning management system for socially disadvantaged...journalBEEI
This study aims to develop Moodle-based LMS with customized learning content and modified user interface to facilitate pedagogical processes during covid-19 pandemic and investigate how teachers of socially disadvantaged schools perceived usability and technology acceptance. Co-design process was conducted with two activities: 1) need assessment phase using an online survey and interview session with the teachers and 2) the development phase of the LMS. The system was evaluated by 30 teachers from socially disadvantaged schools for relevance to their distance learning activities. We employed computer software usability questionnaire (CSUQ) to measure perceived usability and the technology acceptance model (TAM) with insertion of 3 original variables (i.e., perceived usefulness, perceived ease of use, and intention to use) and 5 external variables (i.e., attitude toward the system, perceived interaction, self-efficacy, user interface design, and course design). The average CSUQ rating exceeded 5.0 of 7 point-scale, indicated that teachers agreed that the information quality, interaction quality, and user interface quality were clear and easy to understand. TAM results concluded that the LMS design was judged to be usable, interactive, and well-developed. Teachers reported an effective user interface that allows effective teaching operations and lead to the system adoption in immediate time.
Understanding the role of individual learner in adaptive and personalized e-l...journalBEEI
Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.
Prototype mobile contactless transaction system in traditional markets to sup...journalBEEI
1) Researchers developed a prototype contactless transaction system using QR codes and digital payments to support physical distancing during the COVID-19 pandemic in traditional markets.
2) The system allows sellers and buyers in traditional markets to conduct fast, secure transactions via smartphones without direct cash exchange. Buyers scan sellers' QR codes to view product details and make e-wallet payments.
3) Testing showed the system's functions worked properly and users found it easy to use and useful for supporting contactless transactions and digital transformation of traditional markets. However, further development is needed to increase trust in digital payments for users unfamiliar with the technology.
Wireless HART stack using multiprocessor technique with laxity algorithmjournalBEEI
The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.
Implementation of double-layer loaded on octagon microstrip yagi antennajournalBEEI
This document describes the implementation of a double-layer structure on an octagon microstrip yagi antenna (OMYA) to improve its performance at 5.8 GHz. The double-layer consists of two double positive (DPS) substrates placed above the OMYA. Simulation and experimental results show that the double-layer configuration increases the gain of the OMYA by 2.5 dB compared to without the double-layer. The measured bandwidth of the OMYA with double-layer is 14.6%, indicating the double-layer can increase both the gain and bandwidth of the OMYA.
The calculation of the field of an antenna located near the human headjournalBEEI
In this work, a numerical calculation was carried out in one of the universal programs for automatic electro-dynamic design. The calculation is aimed at obtaining numerical values for specific absorbed power (SAR). It is the SAR value that can be used to determine the effect of the antenna of a wireless device on biological objects; the dipole parameters will be selected for GSM1800. Investigation of the influence of distance to a cell phone on radiation shows that absorbed in the head of a person the effect of electromagnetic radiation on the brain decreases by three times this is a very important result the SAR value has decreased by almost three times it is acceptable results.
Exact secure outage probability performance of uplinkdownlink multiple access...journalBEEI
In this paper, we study uplink-downlink non-orthogonal multiple access (NOMA) systems by considering the secure performance at the physical layer. In the considered system model, the base station acts a relay to allow two users at the left side communicate with two users at the right side. By considering imperfect channel state information (CSI), the secure performance need be studied since an eavesdropper wants to overhear signals processed at the downlink. To provide secure performance metric, we derive exact expressions of secrecy outage probability (SOP) and and evaluating the impacts of main parameters on SOP metric. The important finding is that we can achieve the higher secrecy performance at high signal to noise ratio (SNR). Moreover, the numerical results demonstrate that the SOP tends to a constant at high SNR. Finally, our results show that the power allocation factors, target rates are main factors affecting to the secrecy performance of considered uplink-downlink NOMA systems.
Design of a dual-band antenna for energy harvesting applicationjournalBEEI
This report presents an investigation on how to improve the current dual-band antenna to enhance the better result of the antenna parameters for energy harvesting application. Besides that, to develop a new design and validate the antenna frequencies that will operate at 2.4 GHz and 5.4 GHz. At 5.4 GHz, more data can be transmitted compare to 2.4 GHz. However, 2.4 GHz has long distance of radiation, so it can be used when far away from the antenna module compare to 5 GHz that has short distance in radiation. The development of this project includes the scope of designing and testing of antenna using computer simulation technology (CST) 2018 software and vector network analyzer (VNA) equipment. In the process of designing, fundamental parameters of antenna are being measured and validated, in purpose to identify the better antenna performance.
Transforming data-centric eXtensible markup language into relational database...journalBEEI
eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.
Key performance requirement of future next wireless networks (6G)journalBEEI
The document provides an overview of the key performance indicators (KPIs) for 6G wireless networks compared to 5G networks. Some of the major KPIs discussed for 6G include: achieving data rates of up to 1 Tbps and individual user data rates up to 100 Gbps; reducing latency below 10 milliseconds; supporting up to 10 million connected devices per square kilometer; improving spectral efficiency by up to 100 times through technologies like terahertz communications and smart surfaces; and achieving an energy efficiency of 1 pico-joule per bit transmitted through techniques like wireless power transmission and energy harvesting. The document outlines how 6G aims to integrate terrestrial, aerial and maritime communications into a single network to provide ubiquitous connectivity with higher
Noise resistance territorial intensity-based optical flow using inverse confi...journalBEEI
This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.
Modeling climate phenomenon with software grids analysis and display system i...journalBEEI
This study aims to model climate change based on rainfall, air temperature, pressure, humidity and wind with grADS software and create a global warming module. This research uses 3D model, define, design, and develop. The results of the modeling of the five climate elements consist of the annual average temperature in Indonesia in 2009-2015 which is between 29oC to 30.1oC, the horizontal distribution of the annual average pressure in Indonesia in 2009-2018 is between 800 mBar to 1000 mBar, the horizontal distribution the average annual humidity in Indonesia in 2009 and 2011 ranged between 27-57, in 2012-2015, 2017 and 2018 it ranged between 30-60, during the East Monsoon, the wind circulation moved from northern Indonesia to the southern region Indonesia. During the west monsoon, the wind circulation moves from the southern part of Indonesia to the northern part of Indonesia. The global warming module for SMA/MA produced is feasible to use, this is in accordance with the value given by the validate of 69 which is in the appropriate category and the response of teachers and students through a 91% questionnaire.
An approach of re-organizing input dataset to enhance the quality of emotion ...journalBEEI
The purpose of this paper is to propose an approach of re-organizing input data to recognize emotion based on short signal segments and increase the quality of emotional recognition using physiological signals. MIT's long physiological signal set was divided into two new datasets, with shorter and overlapped segments. Three different classification methods (support vector machine, random forest, and multilayer perceptron) were implemented to identify eight emotional states based on statistical features of each segment in these two datasets. By re-organizing the input dataset, the quality of recognition results was enhanced. The random forest shows the best classification result among three implemented classification methods, with an accuracy of 97.72% for eight emotional states, on the overlapped dataset. This approach shows that, by re-organizing the input dataset, the high accuracy of recognition results can be achieved without the use of EEG and ECG signals.
Parking detection system using background subtraction and HSV color segmentationjournalBEEI
Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.
Quality of service performances of video and voice transmission in universal ...journalBEEI
The universal mobile telecommunications system (UMTS) has distinct benefits in that it supports a wide range of quality of service (QoS) criteria that users require in order to fulfill their requirements. The transmission of video and audio in real-time applications places a high demand on the cellular network, therefore QoS is a major problem in these applications. The ability to provide QoS in the UMTS backbone network necessitates an active QoS mechanism in order to maintain the necessary level of convenience on UMTS networks. For UMTS networks, investigation models for end-to-end QoS, total transmitted and received data, packet loss, and throughput providing techniques are run and assessed and the simulation results are examined. According to the results, appropriate QoS adaption allows for specific voice and video transmission. Finally, by analyzing existing QoS parameters, the QoS performance of 4G/UMTS networks may be improved.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
2. Bulletin of Electr Eng and Inf ISSN: 2302-9285
Solar PV parameter estimation using multi-objective optimisation (Nikita Rawat)
1199
c. To obtain accurate design specification of the power conditioning equipment connected with the solar
PV, and [5].
d. To simulate maximum power tracking algorithm, precisely.
Generally, existing methods were divided into three categories in the research, such as: Analytical
methods, Numerical methods, and Metaheuristics methods.
Usually, the manufacturer provides the remarkable points, such as, ‘voltage at the maximum power point
(VMP)’, ‘current at the maximum power point (IMP)’, ‘short-circuit current (ISC), and ‘open-circuit voltage
(VOC)’ in their data sheet [6]. The accuracy of parameter estimation through analytical methods relies heavily
on the accurate emplacement of these remarkable points or known parameters on solar PV output
characteristics [3, 7].
Among the various existing numerical methods, Newton-Raphson (NR) method [8], Lambert W
function [9], and Gauss-Seidel (GS) method [10], were frequently considered in estimation of accurate
electrical parameters of solar PV. Though these numerical methods have a higher level of accuracy than the
analytical methods, these methods suffer from extensive computation time for the convergence also they
converge to local maxima instead of global in case of wrong selection of initial values especially in NR and
GS methods [8, 11].
Although Metaheuristics Algorithms based approaches play a vital role in the extraction of electrical
model parameters of solar PV, unfortunately, the speed of convergence is found to be low in genetic
algorithm (GA) [3]. Also, the GA based approach is found to be unsuitable for highly interactive fitness
function [3]. The swap between the initial temperature and cooling schedule is a major issue in simulating
annealing (SA) [12]. Though particle swarm optimization (PSO) outperforms SA and GA [13], at the same
time it is found inept to track accurate characteristics as provided in manufacturer’s datasheet. Also, the
uniformity of estimated electrical model parameter cannot be assured through PSO [3, 13-15].
Besides the shortfalls as discussed above, most of the aforementioned methods consider the task of
parameter estimations as a single objective optimisation i.e., the error between the real and estimated or
predicted current at a known voltage is considered as the objective function. Indeed, parameter estimation of
solar PV is a multi-objective optimisation (MOO) task, wherein, accurate values of all five parameters, such
as IPh, Io, VT, Rs, and Rp are highly desirable to achieve characteristics exactly in tune with the real
characteristics of solar PV. Unfortunately, in most of the research works, all of these five unknown
parameters were not considered to reduce computational complexities.
Therefore, to rectify the inconsistencies prevailing in these methods, the present study proposes an
accurate method of parameters extraction based on MOO [16], to accurately describe the solar PV output
characteristics.
2. THE SOLAR PV CELL MODEL
The single diode model or Rp-model of solar PV is shown in Figure 1 which has been used in the
present work to investigate the performance of solar PV. Although, there are two and three diode models of
solar PV but these models have simultaneously increased the computational complexity due to a large
number of unknown parameters. Also, a significant trade-off between accuracy and computational simplicity
is achieved in Rp-model [11].
Figure 1. Single-diode solar PV model
The characteristic equation of Rp-model, as given in (1), provides the relation between the output
voltage (V) and current (I) in terms of unknown parameters.
(1)
exp 1
s s
Ph o
T p
V R I V R I
I I I
V R
3. ISSN: 2302-9285
Bulletin of Electr Eng and Inf, Vol. 8, No. 4, December 2019 : 1198 – 1205
1200
Where, VT=(αKTNs)/e is in terms of diode ideality factor (α), electron charge (e), Boltzmann’s constant (K),
number of solar PV cell in series (Ns), and temperature (T).
3. PROPOSED OBJECTIVE FUNCTION
The output characteristic of a PV cell relies on five parameters, namely, IPh, Io, VT, Rs, and Rp, as
given in (1). To estimate these unknown parameters, five equations, as given in (2), (3), (4), (6), and (8), can
be derived from (1) as [17]:
a. Under short circuit condition, i.e., when V=0, I=ISC, the relation, as given in (1), can be written as:
(2)
b. Under open circuit condition, i.e., when I=0, V=VOC, the relation as given in (1), can be derived as:
(3)
c. At maximum power point (MPP), i.e., when I=IMP, V=VMP, the relation, as given in (1), can be written
as:
(4)
d. The slope at MPP on P-V curve will be parallel to the voltage axis and hence it is found that
(5)
On solving (5) the forth equation can be derived as:
(6)
e. The final equation is derived by calculating the slope of the I-V characteristic curve. The slope of the
I-V characteristic curve is derived by differentiating the output current with respect to the output voltage
under short circuit condition.
(7)
On solving (7) the fifth equation can be derived as:
(8)
The (2), (3), (4), (6), and (8), have been combined to define objective function, f(x), as:
(9)
Now, the MOO problem is formulated as:
(10)
1( ) exp 1
SC S SC S
SC Ph o
T P
I R I R
f x I I I
V R
2 ( ) exp 1
OC OC
Ph o
T p
V V
f x I I
V R
3
( )
( ) exp 1
MP MP s MP MP S
MP Ph o
T P
V I R V I R
f x I I I
V R
MP
MPP MP
I
dI
dV V
4
1
( ) ( ) exp
o MP MP s
MP MP MP s
T T P
I V I R
f x I V I R
V V R
1
SC
I I p
dI
dV R
5 ( ) exp
s o SC s
p s
p T T
R I I R
f x R R
R V V
1 2 3 4 5
( ) ( ), ( ), ( ), ( ), ( )
f x f x f x f x f x f x
1 2 3 4 5
( ( ), ( ), ( ), ( ), ( ))
min f x f x f x f x f x
4. Bulletin of Electr Eng and Inf ISSN: 2302-9285
Solar PV parameter estimation using multi-objective optimisation (Nikita Rawat)
1201
Here, x is the array of decision variables {x1, x2, x3, x4, x5}. In the proposed work x1, x2, x3, x4, and x5
represents IPh, Io, Rs, Rp and VT, respectively.
3.1. Proposed parameter estimation method
Various MOO methods generated from GA, such as the Vector-Evaluated Genetic Algorithm,
Strength Pareto Metaheuristics Algorithm, Pareto Archived Evolution Strategy, classical non-dominated
sorting-based multi-objective evolutionary algorithm, and NSGA-II, were tested and verified. Among these
Methods, NSGA-II is considered to be one of the best methods due to its lower computation time and
non-elitism approach. Therefore the present work considers NSGA-II, to evaluate the electrical model
parameters of solar PV [16]. The following steps have been used in the proposed NSGA-II:
a. Initialisation: objective function f (x), as given in (9), main population D, and the input variable and their
ranges are initialized.
b. Non-Domination Sort: NSGA-II uses non-domination sort to sort the initialized main population D. Each
individual p in the main population D has two sets. The set Sp contains the individuals which are
dominated by p whereas set np contains those individuals which are dominated to p. If the individual p
has zero individuals in its set np, then p is assigned to front one (F1) and ranked one.
c. Crowding Distance: As the fitness value or rank is achieved, the next step is to assign the crowding
distance Fi(dj), where Fi is the ith
front counter and dj is the crowding distance of the jth
individual in
front Fi. The crowding distance is the distance between two individuals.
d. Tournament Selection: The selection of the individual is dependent on its individual rank and crowding
distance. The rank of the individual has been checked and the individual with the smallest rank is
selected. In case of similar rank of two individuals, the crowding distance is considered for the selection.
In this case, the individual with larger values of crowding distance, i.e., Fi(dj) , is selected.
e. Genetic Operators: Offspring population is created using the genetic operator’s binary crossover
and mutation.
f. Recombination: The parent population is united with the offspring population and sorted again using
non-domination sorting. The unfit individuals are replaced by the fit one and the original size of the
population is maintained.
4. RESULTS AND DISCUSSION
4.1. Estimation of performance indices for polycrystalline and monocrystalline PV modules
Three polycrystalline and monocrystalline PV module with specifications as given in Tong NT et al
[18] were considered. For proposed method and PSO, similar lower and upper bound values were taken,
while for the NR method, different initial values were used. The electrical model parameters value for the PV
modules found by the proposed method, NR, PSO, and search algorithm is summarised in Table 1.
Figure 2-5 shows the P-V characteristic of the PV modules. It is evident that the P-V curve obtained by the
proposed method for both type of PV cell modules were closest to the MPP. Also, it was observed that NR
was the second best among the other methods but the accuracy of NR method is very much subjected to the
selection of initial values which is evident from Table 1. Table 2 summarises the ARPE calculated for PV
modules. The ARPE for all the PV modules using the proposed method were calculated to be very less. Thus,
from the analysis of all the results it is evident that proposed method outperforms NR, PSO, and search
algorithm in the case of polycrystalline as well as monocrystalline PV modules.
Figure 2. P-V characteristics for WW energy,
AS240-6P30 module
Figure 3. P-V characteristics for Solarworld, Pro.
SW255 module
5. ISSN: 2302-9285
Bulletin of Electr Eng and Inf, Vol. 8, No. 4, December 2019 : 1198 – 1205
1202
Figure 4. P-V characteristics for Nemy, JP270M60
module
Figure 5. P-V characteristics for Solarworld, Plus
SW280 module
Table 1. Extracted parameters for polycrystalline and monocrystalline PV modules by the proposed and
existing methods
PV modules
ParameterE
xtracted
Search
algorithm
[18]
NR
Lower
Bound
Upper
Boun
d
PSO
Proposed
method
Initial
value
Estimated
value
Polycrystalline
WW Energy
AS240-6P30
IPh (A) 8.5705 8.5 8.5707 8 8.6 8.5568 8.5108
Io (A) 0.0074E-6 1E-8 6.83E-9 7E-10 1E-7 3.488E-8 1E-8
Rs (Ω) 5.79E-3 0.3 0.3490 0.1 0.5 0.3450 0.343
Rp (Ω) 94.87 2000 4175.46 50 5000 2047.8 3500.6
VT α=1.1725 1.6 1.7997 0.1 2 1.9489 1.8509
Solarworld,
Pro. SW255
IPh (A) 8.8805 8 8.8807 8 9 8.8565 8.8814
Io (A) 0.026E-6 1E-6 2.317E-8 10E-10 1E-6 2.311E-8 0.1E-7
Rs (Ω) 3.457E-3 0.1 0.21 0.1 0.5 0.2093 0.2277
Rp (Ω) 57.40 2000 2570.3 50 5000 2579.5 3735.8
VT α=1.2554 1 1.9228 0.1 2 1.9237 1.8422
Monocrystalline
Solarworld, Plus
SW280
IPh (A) 9.7109 9.7 9.7112 9 10 9.6748 9.6945
Io (A) 0.019E-6 1E-8 1.735E-8 1E-10 1E-7 1.844E-8 0.9E-8
Rs (Ω) 5.357E-3 0.2 0.3235 0 0.5 0.3447 0.3371
Rp (Ω) 61.07 2000 2714.3 50 1000 2591.2 3563.3
VT α=1.2793 1.8 1.9612 0.1 2 1.9746 1.8991
Nemy,
JP270M60
IPh (A) 9.2002 9 9.2003 9 10 9.3243 9.2035
Io (A) 0.001E-6 1E-9 1.197E-9 1E-10 1E-7 1.069E-9 1E-9
Rs (Ω) 5.01E-3 0.3 0.3015 0.1 0.5 0.2981 0.3061
Rp (Ω) 207.73 9000 9120.8 1000 10000 9393.7 9838.8
VT α=1.1027 1.6 1.6958 0.1 2 1.6826 1.6827
Table 2. Absolute relative power error for polycrystalline modules
PV module
Extraction
methods
Actual Maximum
Power Pactual (W)
Calculated
maximum power
Pcal (W)
Absolute Relative Power Error
ARPE 100(%)
actual cal
actual
P P
P
WW Energy
AS240-6P30
NR 240.097 239.8 1.2370×10-1
PSO 240.097 235.64 0.0185×102
Search algorithm 240.097 243.3 0.0133×102
Proposed Method 240.097 240.15 2.2074×10-2
Solarworld, Pro.
SW255
NR 257.088 256.76 1.2758×10-1
PSO 257.088 256.280 3.1428×10-1
Search algorithm 257.088 250.68 0.0249×102
Proposed Method 257.088 257.15 2.4116×10-2
Solarworld, Plus
SW280
NR 282.984 282.99 2.1202×10-3
PSO 282.984 281.24 6.1628×10-1
Search Algorithm 282.984 282.52 1.6396×10-1
Proposed Method 282.984 282.980 1.4135×10-3
Nemy, JP270M60
NR 269.948 269.793 5.7418×10-2
PSO 269.948 272.94 0.0110×102
Search Algorithm 269.948 277.8 0.029×102
Proposed Method 269.948 269.913 1.2965×10-2
6. Bulletin of Electr Eng and Inf ISSN: 2302-9285
Solar PV parameter estimation using multi-objective optimisation (Nikita Rawat)
1203
4.2. Estimation of performance indices for photowatt PWP201 module
The unknown parameters of Photowatt PWP201 comprising of 36 polycrystalline silicon series
connected cells at T=45˚C [18], is calculated with the proposed method (NSGA-II), NR, GA, PS, SA,
(MPCOA), and (GOFPANM) are outlined in Table 3. Figure 6 shows the P-V curve of the PV module. The
P-V curve obtained by the proposed method is close to the experimental data, in particular at the MPP point.
Further, the values of MPP and ARPE, as estimated with these existing and the proposed method, have been
summarised in Table 4. The ARPE is smaller for the proposed method. Table 5 shows the IAE matrics for
each experimentally measured I-V points. The MAE calculated for the proposed method is 0.1875%, which
is the lowest followed by the MAE calculated by the MPCOA and GOFPANM methods.
The convergence process of the proposed method is shown in Figure 7. The proposed method is
incomplex and does not have parameters that need to be tuned as in the case of PSO, SA, PS, MPCOA, and
GOFPANM methods.
Table 3. Photowatt PWP201 module parameters extracted by the proposed method and compared with
various methods
Parameters
Extracted
SA [12] NR [8] PS [19] GA [20] MPCOA [21]
GOFPANM
[22]
Proposed
method
IPh(A) 1.0331 1.0318 1.0313 1.0441 1.03188 1.0305143 1.0301
Io(µA) 3.6642 3.2875 3.1756 3.436 3.3737 3.4822631 0.79851
Rs(Ω) 1.1989 1.2057 1.2053 1.1968 1.20295 1.2012710 1.4944
Rp(Ω) 8333.333 555.56 714.29 555.556 849.693 981.98232 785.1624
Nsα 48.8211 48.45 48.289 48.5862 48.5065 48.6428351
43.583919
(VT=1.1949)
Figure 6. The P-V curve of the reference module Photowatt PWP201 by the proposed method and eight other
existing method
Figure 7. The convergence curve of Photowatt PWP201 by the proposed method (f(x)=0.0373 at 50
generation and 5000 population)
7. ISSN: 2302-9285
Bulletin of Electr Eng and Inf, Vol. 8, No. 4, December 2019 : 1198 – 1205
1204
Table 4. Absolute relative power error for Photowatt PWP201 module
Methods *Pactual **Pcal ARPE
SA 11.5403 11.6958 0.01347×102
NR 11.5403 11.4441 8.60462×10-1
PS 11.5403 11.5025 3.27547×10-1
GA 11.5403 11.5752 3.02418×10-1
MPCOA 11.5403 11.5293 9.53181×10-2
GOFPANM 11.5403 11.5374 2.51293×10-2
Proposed
method
11.5403 11.5375 2.42628×10-2
*Actual Maximum Power, Pactual (W)
**Calculated maximum power, Pcal (W)
Table 5. The error matrics measurement by the proposed method and other existing methods for Photowatt
PWP201 module
Measured value
Calculate
d I(A)
Current Absolute Error (IAE%) matrics
V (V) I (A)
Proposed
Method
Proposed
method
GOFPANM
[20]
MPCO
A [18]
GA
[22]
PS [19] NR [8] SA [12]
1 0.1248 1.0315 1.028 0.340467 0.233213488 0.119 0.988 0.207 0.213 0.006
2 1.8093 1.03 1.0269 0.301879 0.253065992 0.168 0.845 0.294 0.367 0.062
3 3.3511 1.026 1.0258 0.019497 0.029248318 0.037 0.966 0.123 0.258 0.137
4 4.7622 1.022 1.0219 0.009786 0.2050581 0.247 1.099 0.055 0.138 0.341
5 6.0538 1.018 1.02 0.196078 0.42062017 0.443 1.224 0.222 0.023 0.531
6 7.2364 1.0155 1.0177 0.216174 0.431414845 0.440 1.155 0.196 0.099 0.521
7 8.3189 1.014 1.0142 0.01972 0.226311129 0.223 0.876 0.041 0.383 0.292
8 9.3097 1.01 1.0099 0.009902 0.049480455 0.029 0.626 0.250 0.636 0.082
9 10.2163 1.0035 1.0006 0.289826 0.289826104 0.307 0.237 0.600 1.028 0.281
10 11.0449 0.988 0.9855 0.253678 0.345317896 0.368 0.135 0.668 1.139 0.374
11 11.8018 0.963 0.9609 0.218545 0.354314298 0.371 0.102 0.675 1.189 0.418
12 12.4929 0.9255 0.9237 0.194868 0.249133449 0.288 0.174 0.587 1.144 0.378
13 13.1231 0.8725 0.8712 0.149219 0.068720651 0.008 0.495 0.269 0.867 0.115
14 13.6983 0.8075 0.8023 0.648137 0.061881188 0.035 0.505 0.286 0.919 0.188
15 14.2221 0.7265 0.7227 0.525806 0.137457045 0.194 0.868 0.016 0.648 0.061
16 14.6995 0.6345 0.6339 0.094652 0.188768287 0.309 1.154 0.198 0.487 0.192
17 15.1346 0.5345 0.5342 0.056159 0.037404152 0.226 1.240 0.115 0.575 0.067
18 15.5311 0.4275 0.427 0.117096 0.11682243 0.311 1.666 0.270 0.405 0.187
19 15.8929 0.3185 0.3184 0.031407 -0.031407035 0.057 1.738 0.122 0.735 0.232
20 16.2229 0.2085 0.2083 0.096015 0.143678161 0.302 2.030 0.775 1.222 0.906
21 16.5241 0.101 0.1009 0.099108 0.296150049 0.990 0.520 5.153 5.002 5.287
Total IAE% 3.888021 4.10647917 5.481 18.648 11.13 21.63 5.775
MAE (%) 0.185144 0.195546 0.261 0.888 0.530 1.030 0.275
MAE of 3 points near MPP (%) 0.1875 0.22405 0.2223 0.257 0.510 1.0666 0.3036
5. CONCLUSION
The present investigation has considered MOO algorithm NSGA-II for estimating the electrical
model parameters of solar PV modules. In comparison to the existing methods such as NR, GA, PSO, PS,
SA, MPCOA, and GOFPANM, the proposed NSGA-II method outperformed and provided a better P-V and
I-V curve, as well as a lesser value of ARPE and MAE. Henceforth, it is inferred that the MOO-based
approach can be recommended as one of the most accurate tools for the parameters estimation of solar PV.
REFERENCE
[1] M. A. Jusoh, M. F. Tajuddin, S. M. Ayob & M. A. Roslan., "Maximum Power Point Tracking RCharge Controller
for Standalone PV System," Telkomnika (Telecommunication, Computing, Electronics and Control), vol. 16(4), pp.
1413-26, 2018.
[2] M. Mardlijah and Z. Zuhri. "Solar Panel Control System Using an Intelligent Control: T2FSMC and Firefly
Algorithm," Telkomnika (Telecommunication Computing Electronics and Control), vol. 16(6), pp. 2988-2998,
2018.
[3] K. Ishaque, Z. Salam, S. Mekhilef & A. Shamsudin., "Parameter extraction of solar photovoltaic modules using
penalty-based differential evolution," Applied Energy, vol. 99, pp. 297-308, 2012.
[4] S. Shongwe and M. Hanif, "Comparative Analysis of Different Single-Diode PV Modeling Methods," in IEEE
Journal of Photovoltaics, vol. 5, no. 3, pp. 938-946, May 2015.
[5] F. Attivissimo, A. Di Nisio, M. Savino and M. Spadavecchia, "Uncertainty Analysis in Photovoltaic Cell Parameter
Estimation," in IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 5, pp. 1334-1342, May 2012.
8. Bulletin of Electr Eng and Inf ISSN: 2302-9285
Solar PV parameter estimation using multi-objective optimisation (Nikita Rawat)
1205
[6] D. S. H. Chan and J. C. H. Phang, "Analytical methods for the extraction of solar-cell single- and double-diode
model parameters from I-V characteristics," in IEEE Transactions on Electron Devices, vol. 34, no. 2, pp. 286-293,
Feb. 1987.
[7] J. Accarino, G. Petrone, C. A. Ramos-Paja and G. Spagnuolo, "Symbolic algebra for the calculation of the series
and parallel resistances in PV module model," 2013 International Conference on Clean Electrical Power (ICCEP),
Alghero, 2013, pp. 62-66.
[8] T. Easwarakhanthan, J. Bottin, I. Bouhouch & C. Boutrit., "Nonlinear minimization algorithm for determining the
solar cell parameters with microcomputers," International Journal of Solar Energy, vol. 4(1), pp. 1-2, 1986.
[9] F. Ghani and M. Duke. "Numerical determination of parasitic resistances of a solar cell using the Lambert
W-function," Solar Energy, vol. 85(9), pp. 2386-94, 2011.
[10] S. Shongwe and M. Hanif, "Gauss-Seidel iteration based parameter estimation for a single diode model of a PV
module," 2015 IEEE Electrical Power and Energy Conference (EPEC), London, ON, 2015, pp. 278-284.
[11] M. G. Villalva, J. R. Gazoli and E. R. Filho, "Comprehensive Approach to Modeling and Simulation of
Photovoltaic Arrays," in IEEE Transactions on Power Electronics, vol. 24, no. 5, pp. 1198-1208, May 2009.
[12] K. M. El-Naggar, M. R. AlRashidi, M. F. AlHajri & A. K. Al-Othman., "Simulated annealing algorithm for
photovoltaic parameters identification," Solar Energy, vol. 86(1), pp. 266-74, 2012.
[13] M. Ye, X. Wang & Y. Xu., "Parameter extraction of solar cells using particle swarm optimization," Journal of
Applied Physics, vol. 105(9), pp. 094502, 2009.
[14] K. Ishaque and Z. Salam. "An improved modeling method to determine the model parameters of photovoltaic (PV)
modules using differential evolution (DE)," Solar energy, vol. 85(9), pp. 2349-59, 2011.
[15] J. J. Soon and K. Low, "Photovoltaic Model Identification Using Particle Swarm Optimization With Inverse Barrier
Constraint," in IEEE Transactions on Power Electronics, vol. 27, no. 9, pp. 3975-3983, Sept. 2012.
[16] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II,"
in IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, April 2002.
[17] U. Jadli, P. Thakur and R. D. Shukla, "A New Parameter Estimation Method of Solar Photovoltaic," in IEEE
Journal of Photovoltaics, vol. 8, no. 1, pp. 239-247, Jan. 2018.
[18] N. T. Tong and W. Pora. "A parameter extraction technique exploiting intrinsic properties of solar cells," Applied
energy, vol. 15(176), pp. 104-15, 2016
[19] M. F. Al Hajri, K. M. El-Naggar, M. R. AlRashidi & A. K. Al-Othman., "Optimal extraction of solar cell
parameters using pattern search," Renewable Energy, vol. 44, pp. 238-45, 2012.
[20] M. R. Al Rashidi, M. F. Al Hajri, K. M. El-Naggar & A. K Al-Othman., "A new estimation approach for
determining the I–V characteristics of solar cells," Solar Energy, vol. 85(7), pp. 1543-50, 2011.
[21] Yuan X, Xiang Y & He Y., "Parameter extraction of solar cell models using mutative-scale parallel chaos
optimization algorithm," Solar Energy, vol. 108, pp. 238-51, 2014 Oct 1.
[22] S. Xu and Y. Wang, "Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm,"
Energy Conversion and Management, vol. 144, pp. 53-68, 2017.