This document presents a modified particle swarm optimization (PSO) algorithm to improve the tracking of the maximum power point (MPP) in photovoltaic (PV) arrays. The traditional PSO algorithm can track the MPP under shaded conditions but has issues with tracking speed and optimization process. The proposed modified PSO partitions the duty cycle into two parts - the previous duty ratio is adjusted by a linearization factor, and two new duty cycles are perturbed by a constant value to search the PV curve for the MPP. Simulation results show the modified PSO approach improves tracking speed, accuracy, and efficiency compared to the traditional PSO algorithm.
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.
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- 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.
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.
Comparison between neural network and P&O method in optimizing MPPT control f...IJECEIAES
The demand for renewable energy has increased because it is considered a clean energy and does not result in any pollution or emission of toxic gases that negatively affect the environment and human health also requiring little maintenance, and emitting no noise, so it is necessary to develop this type of energy and increase its production capacity. In this research a design of maximum power point tracking (MPPT) control method using Neural Network (NN) for photovoltaic system is presented. First we design a standalone PV system linked to dc boost chopper with MPPT by perturbation and observation P&O technique, and then a design of MPPT by using ANN for the same system is presented. Comparative between two control methods are studied. The results explained in constant and adjustable weather settings such as irradiation and temperature. The results exposed that the proposed MPPT by ANN control can improve the PV array efficiency by reduce the oscillation around the MPP that accure in P&O method and so decreases the power losses. As well as decrease the the overshot that accure in transient response, and hence improving the performance of the solar cell.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
Ant Colony Optimization for Optimal Photovoltaic Array Reconfiguration under ...IRJET Journal
This document presents an optimization approach for optimal reconfiguration of a photovoltaic (PV) array under partial shading conditions. The approach uses Ant Colony Optimization (ACO) to determine the optimal electrical connections between PV modules in a total cross-tied array layout. The objectives are to maximize power output and minimize switching operations. The ACO algorithm considers factors like irradiance levels and switching costs to find a configuration that distributes shade uniformly and extracts more power than a static layout. Simulation results show the ACO approach enhances power output compared to the initial cross-tied configuration.
Design of Hybrid Solar Wind Energy System in a Microgrid with MPPT Techniques IJECEIAES
DC Microgrid is one feasible and effective solution to integrate renewable energy sources as well as to supply electricity. This paper proposes a DC microgrid with enhanced Maximum Power Point Tracking (MPPT) techniques for wind and solar energy systems. In this paper, the PV system power generation is enhanced by introducing a two-model MPPT technique that combines incremental conductance and constant voltage MPPT algorithms. Also, for the Wind Energy Conversion System (WECS) with pitch angle controlling technique, an Optimal Power Control MPPT technique is added. The Space Vector Pulse Width Modulation technique is introduced on grid side converter to improve the supply to the grid. The performance of proposed system is analyzed and the efficiency obtained with these methods is enhanced as compared with the previous methods.
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.
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- 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.
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.
Comparison between neural network and P&O method in optimizing MPPT control f...IJECEIAES
The demand for renewable energy has increased because it is considered a clean energy and does not result in any pollution or emission of toxic gases that negatively affect the environment and human health also requiring little maintenance, and emitting no noise, so it is necessary to develop this type of energy and increase its production capacity. In this research a design of maximum power point tracking (MPPT) control method using Neural Network (NN) for photovoltaic system is presented. First we design a standalone PV system linked to dc boost chopper with MPPT by perturbation and observation P&O technique, and then a design of MPPT by using ANN for the same system is presented. Comparative between two control methods are studied. The results explained in constant and adjustable weather settings such as irradiation and temperature. The results exposed that the proposed MPPT by ANN control can improve the PV array efficiency by reduce the oscillation around the MPP that accure in P&O method and so decreases the power losses. As well as decrease the the overshot that accure in transient response, and hence improving the performance of the solar cell.
The significance of the solar energy is to intensify the effectiveness of the Solar Panel with the use of a primordial solar tracking system. Here we propounded a solar positioning system with the use of the global positioning system (GPS) , artificial neural network (ANN) and image processing (IP) . The azimuth angle of the sun is evaluated using GPS which provide latitude, date, longitude and time. The image processing used to find sun image through which centroid of sun is calculated and finally by comparing the centroid of sun with GPS quadrate to achieve optimum tracking point. Weather conditions and situation observed through AI decision making with the help of IP algorithms. The presented advance adaptation is analyzed and established via experimental effects which might be made available on the memory of the cloud carrier for systematization. The proposed system improve power gain by 59.21% and 10.32% compare to stable system (SS) and two-axis solar following system (TASF) respectively. The reduced tracking error of IoT based Two-axis solar following system (IoT-TASF) reduces their azimuth angle error by 0.20 degree.
Ant Colony Optimization for Optimal Photovoltaic Array Reconfiguration under ...IRJET Journal
This document presents an optimization approach for optimal reconfiguration of a photovoltaic (PV) array under partial shading conditions. The approach uses Ant Colony Optimization (ACO) to determine the optimal electrical connections between PV modules in a total cross-tied array layout. The objectives are to maximize power output and minimize switching operations. The ACO algorithm considers factors like irradiance levels and switching costs to find a configuration that distributes shade uniformly and extracts more power than a static layout. Simulation results show the ACO approach enhances power output compared to the initial cross-tied configuration.
Design of Hybrid Solar Wind Energy System in a Microgrid with MPPT Techniques IJECEIAES
DC Microgrid is one feasible and effective solution to integrate renewable energy sources as well as to supply electricity. This paper proposes a DC microgrid with enhanced Maximum Power Point Tracking (MPPT) techniques for wind and solar energy systems. In this paper, the PV system power generation is enhanced by introducing a two-model MPPT technique that combines incremental conductance and constant voltage MPPT algorithms. Also, for the Wind Energy Conversion System (WECS) with pitch angle controlling technique, an Optimal Power Control MPPT technique is added. The Space Vector Pulse Width Modulation technique is introduced on grid side converter to improve the supply to the grid. The performance of proposed system is analyzed and the efficiency obtained with these methods is enhanced as compared with the previous methods.
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.
Solar PV parameter estimation using multi-objective optimisationjournalBEEI
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
This document presents a study on modeling a photovoltaic system with maximum power point tracking (MPPT) control using neural networks. It discusses modeling the photovoltaic module and cell using equations. An artificial neural network model with three layers (input, hidden, output) is proposed to identify the maximum power point. Simulation results using Matlab/Simulink show the effectiveness of the neural network technique in improving photovoltaic system performance and maximizing power extraction compared to conventional MPPT methods. The document also analyzes how temperature and solar radiation influence the current-voltage and power-voltage characteristics of the photovoltaic module.
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.
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.
Optimal Control Strategy for a Solar Photovoltaic Power System using MATLAB S...IRJET Journal
This document summarizes a research paper that proposes an improved incremental conductance (InC) algorithm for maximum power point tracking in solar photovoltaic systems. The paper presents simulations comparing the improved InC algorithm to traditional perturb and observe and InC algorithms under varying solar irradiation and temperature conditions. The results show the improved InC algorithm more accurately tracks the maximum power point and maintains higher output power compared to the other algorithms under different operating conditions.
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
Comprehensive Review on Maximum Power Point Tracking Methods for SPV SystemIRJET Journal
This document reviews over 30 maximum power point tracking (MPPT) methods for solar photovoltaic systems. It provides an overview of various conventional and advanced MPPT techniques, including perturb and observe, incremental conductance, fuzzy logic control, and evolutionary algorithms. The document analyzes and compares the performance of these methods in terms of tracking speed, efficiency under changing weather conditions, ability to handle partial shading, and complexity of implementation. It aims to help researchers select the most suitable MPPT technique for their application.
Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.
Impact of compressed air energy storage system into diesel power plant with w...IJECEIAES
The wind energy plays an important role in power system because of its renewable, clean and free energy. However, the penetration of wind power (WP) into the power grid system (PGS) requires an efficient energy storage systems (ESS). compressed air energy storage (CAES) system is one of the most ESS technologies which can alleviate the intermittent nature of the renewable energy sources (RES). Nyala city power plant in Sudan has been chosen as a case study because the power supply by the existing power plant is expensive due to high costs for fuel transport and the reliability of power supply is low due to uncertain fuel provision. This paper presents a formulation of security-constrained unit commitment (SCUC) of diesel power plant (DPP) with the integration of CAES and PW. The optimization problem is modeled and coded in MATLAB which solved with solver GORUBI 8.0. The results show that the proposed model is suitable for integration of renewable energy sources (RES) into PGS with ESS and helpful in power system operation management.
The document summarizes a research paper that proposes using a battery energy storage system (BESS) with droop control to reduce frequency fluctuations in a multi-machine power system connected to a large-scale photovoltaic (PV) plant. The paper develops a droop control strategy for the BESS that incorporates a frequency error signal and dead-band. Simulation results using PSCAD/EMTDC software show that the proposed droop control-based BESS can efficiently curtail frequency oscillations caused by fluctuations in PV power injection due to changing solar irradiance.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
Photovoltaic (PV) technology is one of the important renewable energy resources as it is pollution free and clean. PV systems have a high cost of energy and low eciency, consequently, they not made it fully attractive as an alternative option for electricity users. It is essential that PV systems are operated to extract the maximum possible power at all times. Maximum Power Point (MPP) changes with atmospheric conditions (radiation and temperature), it is dicult to sustain MPP at all atmospheric levels. Many Maximum Power Point Tracking (MPPT) have been developed and implemented. These methods varied according to several aspects such as a number of sensors used, complexity, accuracy, speed, ease of hardware implementation, cost and tracking eciency. The MPPT techniques presented in the literature indicate that Variable step size of Perturb & Observe (VP&O), Variable step size of Incremental Conductance (VINC) and Perturb & Observe (P&O) using Fuzzy Logic Controller (FLC) can achieve reliable global MPPT with low cost and complexity and be easily adapted to dierent PV systems. In this paper, we established theoretical and experimental verication of the main MPPT controllers (VP&O, VINC, and P&O using FLC MPPT algorithms) that most cited in the literature. The three MPPT controller has been tested by MATLAB/Simulink to analyze each technique under dierent atmospheric conditions. The experimental results show that the performance of VINC and P&O using FLC is better than VP&O in term of response time.
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
International Journal of Engineering Research and DevelopmentIJERD Editor
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This document compares the performance of an artificial neural network trained with genetic algorithm (ANN-GA) data and a fuzzy logic controller for maximum power point tracking (MPPT) in a grid-connected photovoltaic system. The ANN-GA method uses a genetic algorithm to optimize training data for an artificial neural network controller. Simulation results in Matlab/Simulink show that the ANN-GA controller produces power with fewer fluctuations around the maximum power point and extra power compared to the fuzzy logic controller under different irradiance and temperature conditions. The ANN-GA method also regulates the PV output power well with the grid-connected inverter.
A MATLAB /Simulink Modal of Triple-Junction Solar Cell and MPPT Based on Incr...IJERA Editor
Photovoltaic energy is the most important energy resource since it is clean, pollution free, and unlimited. In
current years, a large number of techniques have been projected for tracking the maximum power point.
Maximum power point tracking is used in photovoltaic systems to maximize the photovoltaic array output
power, irrespective of the temperature and radiation conditions and of the load electrical characteristics the PV
array output power is used to directly control the boost converter, thus reducing the complexity of the system.
The method is based on use of a Incremental conductance of the PV to determine an optimum operating current
for the maximum output power. The implementation of a PV model is based on the triple-junction solar cell in
the form of masked block in Matlab/Simulink software package that has a user-friendly icon. It is fast and
accurate technique to follow the maximum power point. This paper presents a new Matlab/Simulink model of a
PV module and a maximum power point tracking (MPPT) system for high efficiency InGaP/InGaAs/Ge triplejunction
solar cell.
Comparative analysis of evolutionary-based maximum power point tracking for ...IJECEIAES
This document summarizes and compares the performance of three evolutionary algorithms (EAs) - genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO) - for maximum power point tracking (MPPT) under partial shading conditions of a photovoltaic (PV) module. The EAs are tested on a PV module model with different levels of partial shading and variations in population size and generations. Performance is analyzed based on output power, accuracy, tracking time, and effectiveness. The algorithms aim to track the global maximum power point under non-uniform irradiation caused by partial shading, which conventional MPPT cannot handle.
A cost-effective and optimized maximum powerpoint tracking system for the pho...IJECEIAES
Solar energy is naturally available from sun, and it can be extracted by using a photovoltaic (PV) cell. However, solar energy extraction entirely depends on the climatic conditions and angle of rays falling on PV cells. Hence, maximum powerpoint tracking (MPPT) is considered in most areas under variable climatic conditions, which acts as a controller unit for PV cells. MPPT can enhance the efficiency of PV cells. However, designing an MPPT model is challenging as different uncertainties in the climatic condition may lead to more fluctuations in voltage and current in PV cells. Under the shaded condition, the PV cell may have other MPPT points that lead to the PV cell’s low efficiency in analyzing maximum power. Hence, this paper introduces a cost-effective and optimized system for the PV model that can find optimal power and improve PV cells’ efficiency. The proposed system achieves better computational performance with ~35% and ~42% than existing MPPT techniques. The improved particle swarm optimization (PSO) is smoother due to the enhanced form of MPP tracking. Hence, improved PSO takes 0.038 sec while the existing PSO technique takes 0.045 sec to obtain the MPP tracking.
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.
Solar PV parameter estimation using multi-objective optimisationjournalBEEI
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
This document presents a study on modeling a photovoltaic system with maximum power point tracking (MPPT) control using neural networks. It discusses modeling the photovoltaic module and cell using equations. An artificial neural network model with three layers (input, hidden, output) is proposed to identify the maximum power point. Simulation results using Matlab/Simulink show the effectiveness of the neural network technique in improving photovoltaic system performance and maximizing power extraction compared to conventional MPPT methods. The document also analyzes how temperature and solar radiation influence the current-voltage and power-voltage characteristics of the photovoltaic module.
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.
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.
Optimal Control Strategy for a Solar Photovoltaic Power System using MATLAB S...IRJET Journal
This document summarizes a research paper that proposes an improved incremental conductance (InC) algorithm for maximum power point tracking in solar photovoltaic systems. The paper presents simulations comparing the improved InC algorithm to traditional perturb and observe and InC algorithms under varying solar irradiation and temperature conditions. The results show the improved InC algorithm more accurately tracks the maximum power point and maintains higher output power compared to the other algorithms under different operating conditions.
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
Comprehensive Review on Maximum Power Point Tracking Methods for SPV SystemIRJET Journal
This document reviews over 30 maximum power point tracking (MPPT) methods for solar photovoltaic systems. It provides an overview of various conventional and advanced MPPT techniques, including perturb and observe, incremental conductance, fuzzy logic control, and evolutionary algorithms. The document analyzes and compares the performance of these methods in terms of tracking speed, efficiency under changing weather conditions, ability to handle partial shading, and complexity of implementation. It aims to help researchers select the most suitable MPPT technique for their application.
Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.
Impact of compressed air energy storage system into diesel power plant with w...IJECEIAES
The wind energy plays an important role in power system because of its renewable, clean and free energy. However, the penetration of wind power (WP) into the power grid system (PGS) requires an efficient energy storage systems (ESS). compressed air energy storage (CAES) system is one of the most ESS technologies which can alleviate the intermittent nature of the renewable energy sources (RES). Nyala city power plant in Sudan has been chosen as a case study because the power supply by the existing power plant is expensive due to high costs for fuel transport and the reliability of power supply is low due to uncertain fuel provision. This paper presents a formulation of security-constrained unit commitment (SCUC) of diesel power plant (DPP) with the integration of CAES and PW. The optimization problem is modeled and coded in MATLAB which solved with solver GORUBI 8.0. The results show that the proposed model is suitable for integration of renewable energy sources (RES) into PGS with ESS and helpful in power system operation management.
The document summarizes a research paper that proposes using a battery energy storage system (BESS) with droop control to reduce frequency fluctuations in a multi-machine power system connected to a large-scale photovoltaic (PV) plant. The paper develops a droop control strategy for the BESS that incorporates a frequency error signal and dead-band. Simulation results using PSCAD/EMTDC software show that the proposed droop control-based BESS can efficiently curtail frequency oscillations caused by fluctuations in PV power injection due to changing solar irradiance.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
Photovoltaic (PV) technology is one of the important renewable energy resources as it is pollution free and clean. PV systems have a high cost of energy and low eciency, consequently, they not made it fully attractive as an alternative option for electricity users. It is essential that PV systems are operated to extract the maximum possible power at all times. Maximum Power Point (MPP) changes with atmospheric conditions (radiation and temperature), it is dicult to sustain MPP at all atmospheric levels. Many Maximum Power Point Tracking (MPPT) have been developed and implemented. These methods varied according to several aspects such as a number of sensors used, complexity, accuracy, speed, ease of hardware implementation, cost and tracking eciency. The MPPT techniques presented in the literature indicate that Variable step size of Perturb & Observe (VP&O), Variable step size of Incremental Conductance (VINC) and Perturb & Observe (P&O) using Fuzzy Logic Controller (FLC) can achieve reliable global MPPT with low cost and complexity and be easily adapted to dierent PV systems. In this paper, we established theoretical and experimental verication of the main MPPT controllers (VP&O, VINC, and P&O using FLC MPPT algorithms) that most cited in the literature. The three MPPT controller has been tested by MATLAB/Simulink to analyze each technique under dierent atmospheric conditions. The experimental results show that the performance of VINC and P&O using FLC is better than VP&O in term of response time.
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
International Journal of Engineering Research and DevelopmentIJERD Editor
• Electrical, Electronics and Computer Engineering,
• Information Engineering and Technology,
• Mechanical, Industrial and Manufacturing Engineering,
• Automation and Mechatronics Engineering,
• Material and Chemical Engineering,
• Civil and Architecture Engineering,
• Biotechnology and Bio Engineering,
• Environmental Engineering,
• Petroleum and Mining Engineering,
• Marine and Agriculture engineering,
• Aerospace Engineering
This document compares the performance of an artificial neural network trained with genetic algorithm (ANN-GA) data and a fuzzy logic controller for maximum power point tracking (MPPT) in a grid-connected photovoltaic system. The ANN-GA method uses a genetic algorithm to optimize training data for an artificial neural network controller. Simulation results in Matlab/Simulink show that the ANN-GA controller produces power with fewer fluctuations around the maximum power point and extra power compared to the fuzzy logic controller under different irradiance and temperature conditions. The ANN-GA method also regulates the PV output power well with the grid-connected inverter.
A MATLAB /Simulink Modal of Triple-Junction Solar Cell and MPPT Based on Incr...IJERA Editor
Photovoltaic energy is the most important energy resource since it is clean, pollution free, and unlimited. In
current years, a large number of techniques have been projected for tracking the maximum power point.
Maximum power point tracking is used in photovoltaic systems to maximize the photovoltaic array output
power, irrespective of the temperature and radiation conditions and of the load electrical characteristics the PV
array output power is used to directly control the boost converter, thus reducing the complexity of the system.
The method is based on use of a Incremental conductance of the PV to determine an optimum operating current
for the maximum output power. The implementation of a PV model is based on the triple-junction solar cell in
the form of masked block in Matlab/Simulink software package that has a user-friendly icon. It is fast and
accurate technique to follow the maximum power point. This paper presents a new Matlab/Simulink model of a
PV module and a maximum power point tracking (MPPT) system for high efficiency InGaP/InGaAs/Ge triplejunction
solar cell.
Comparative analysis of evolutionary-based maximum power point tracking for ...IJECEIAES
This document summarizes and compares the performance of three evolutionary algorithms (EAs) - genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO) - for maximum power point tracking (MPPT) under partial shading conditions of a photovoltaic (PV) module. The EAs are tested on a PV module model with different levels of partial shading and variations in population size and generations. Performance is analyzed based on output power, accuracy, tracking time, and effectiveness. The algorithms aim to track the global maximum power point under non-uniform irradiation caused by partial shading, which conventional MPPT cannot handle.
A cost-effective and optimized maximum powerpoint tracking system for the pho...IJECEIAES
Solar energy is naturally available from sun, and it can be extracted by using a photovoltaic (PV) cell. However, solar energy extraction entirely depends on the climatic conditions and angle of rays falling on PV cells. Hence, maximum powerpoint tracking (MPPT) is considered in most areas under variable climatic conditions, which acts as a controller unit for PV cells. MPPT can enhance the efficiency of PV cells. However, designing an MPPT model is challenging as different uncertainties in the climatic condition may lead to more fluctuations in voltage and current in PV cells. Under the shaded condition, the PV cell may have other MPPT points that lead to the PV cell’s low efficiency in analyzing maximum power. Hence, this paper introduces a cost-effective and optimized system for the PV model that can find optimal power and improve PV cells’ efficiency. The proposed system achieves better computational performance with ~35% and ~42% than existing MPPT techniques. The improved particle swarm optimization (PSO) is smoother due to the enhanced form of MPP tracking. Hence, improved PSO takes 0.038 sec while the existing PSO technique takes 0.045 sec to obtain the MPP tracking.
Maximum power point tracking based on improved spotted hyena optimizer for s...IJECEIAES
The conventional maximum power point tracking (MPPT) method such as perturb and observe (P&O) under partial shading conditions with non-uniform irradiation, can get trapped on local maximum power point (LMPP) and cannot reach global maximum power point (GMPP). This study proposes a bio-inspired metaheuristic algorithm spotted hyena optimizer (SHO) and improved SHO as a new MPPT technique. The proposed SHO-MPPT and improved SHO-MPPT are used to extract GMPP from solar photovoltaic (PV) arrays operated under uniform irradiation and non-uniform irradiation. Simulation with Powersim (PSIM) and experimental with the emulated PV source were presented. Furthermore, to evaluate the performance of the proposed algorithm, SHO-MPPT is compared with P&O-MPPT and particle swarm optimization (PSO)-MPPT. The SHO-MPPT has an accuracy of 99% and has the good capability, but there are power fluctuations before reaching MPP. Therefore, improved SHO-MPPT was developed to get better results. The improved SHO-MPPT proved high accuracy of 99% and faster than SHO-MPPT and PSO-MPPT in tracking the maximum power point (MPP). Furthermore, there are minor power fluctuations.
Photovoltaic parameters estimation of poly-crystalline and mono-crystalline ...IJECEIAES
Photovoltaic (PV) parameters estimation from the experimental current and voltage data of PV modules is vital for monitoring and evaluating the performance of PV power generation systems. Moreover, the PV parameters can be used to predict current-voltage (I-V) behavior to control the power output of the PV modules. This paper aimed to propose an improved differential evolution (DE) integrated with a dynamic population sizing strategy to estimate the PV module model parameters accurately. This study used two popular PV module technologies, i.e., poly-crystalline and mono-crystalline. The optimized PV parameters were validated with the measured data and compared with other recent meta-heuristic algorithms. The proposed population dynamic differential evolution (PDDE) algorithm demonstrated high accuracy in estimating PV parameters and provided perfect approximations of the measured I-V and power-voltage (P-V) data from real PV modules. The PDDE obtained the best and the mean RMSE value of 2.4251E-03 on the poly-crystalline Photowatt-PWP201, while the best and the mean RMSE value on the mono-crystalline STM6-40/36 was 1.7298E-03. The PDDE algorithm showed outstanding accuracy performance and was competitive with the conventional DE and the existing algorithms in the literature.
An efficient scanning algorithm for photovoltaic systems under partial shadingIJECEIAES
This paper proposes a new maximum power point tracking (MPPT) algorithm for photovoltaic systems connected to three-phase grids under partial shading conditions. The algorithm uses a combined perturb and observe and scanning method to efficiently track the global maximum power point, avoiding getting stuck at local maxima. Simulation results show the proposed algorithm has faster tracking speed and higher accuracy compared to existing methods. A direct power control strategy is also used to synchronize the inverter current with the grid voltage. This ensures successful power injection into the grid even under severe operating conditions.
The document proposes a novel maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems that has fast convergence speed, zero oscillation around the MPP under steady state conditions, and high tracking speed during rapid irradiance changes. The algorithm compares the measured PV panel voltage to a defined MPP voltage range, and directly controls the duty cycle of the boost converter connecting the PV panel to the load to maintain the operating point at the MPP. Simulation results show the proposed algorithm more accurately tracks the MPP with no oscillations compared to perturb and observe, incremental conductance, and fuzzy logic MPPT methods under changing irradiance conditions.
A Reliable Tool Based on the Fuzzy Logic Control Method Applying to the DC/DC...phthanh04
Solar energy performs an important role in electric energy based on renewable energy generation systems when referring to
clear energy. Systems for harvesting renewable energy frequently use DC/DC converters, especially solar photovoltaic systems. The
DC/DC boost converter has been used for converting the output voltage from the solar PV system to the required voltage rating of the
utility grid under the disturbance in the photovoltaic temperature and irradiation level. Because of that, a new maximum power point
tracking based on the fuzzy logic controller (MPPT-FLC) algorithm applying the DC/DC boost converter is developed. The proposed
approach aims toward improving the PV system's performance and tracking effectiveness. This aim can be achieved by adjusting the
DC/DC boost converter's duty cycle to ensure that the PV system operates close to its MPP under varying environmental conditions. The
effectiveness of the proposed method is verified in the off-grid PV system under conditions of the change of irradiation and temperature,
and the comparison of between the proposed method, the incremental conductance (INC), perturb and observe (P&O), and modified P&O
methods is also made. The obtained simulation results show that the MPPT capability significantly improved and achieved the highest
MPPT efficiency of 99.999% and an average efficiency of 99.98% in total when applying the proposed method.
This document summarizes a simulation study comparing the performance of three maximum power point tracking (MPPT) algorithms - incremental conductance, perturb and observe, and fuzzy logic control - for a 100 kW photovoltaic system connected to the electrical grid. The system was simulated in MATLAB/Simulink under varying irradiance conditions. Graphs of solar irradiance, PV voltage, duty cycle, modulation index, DC link voltage, grid voltage, grid current, and output power are presented for each MPPT algorithm to analyze and compare their performance.
This document presents the seminar details for a project on maximum power point tracking (MPPT) for a photovoltaic system. The project aims to compare the Buck-boost, Cuk, Sepic and Zeta DC-DC converters for use in an MPPT system using an incremental conduction MPPT algorithm. The methodology will involve simulating the four converters in MATLAB, observing their output characteristics, and selecting the best converter. A literature review covers previous work on MPPT algorithms and converter selection. The project schedule outlines tasks over 10 months including simulation implementation, comparisons of results, and final submission.
A Critical Review of Various MPPT Methods of Solar PV SystemIRJET Journal
This document provides a review of various maximum power point tracking (MPPT) methods for solar photovoltaic (PV) systems. It begins with an abstract that outlines the goals of providing a classification and comparison of MPPT methods. It then discusses the PV module characteristics and how factors like temperature, irradiance, and partial shading affect the power output. The need for an MPPT controller to optimize power extraction under changing conditions is described. Finally, it provides an overview of different MPPT algorithms and comparisons that have been reported in other literature.
MPPT-Based Control Algorithm for PV System Using iteration-PSO under Irregula...AZOJETE UNIMAID
This document describes a proposed Iteration Particle Swarm Optimization (IPSO) algorithm for maximum power point tracking (MPPT) control of photovoltaic (PV) systems under irregular shadow conditions. The conventional PSO algorithm has difficulty tracking the global maximum power point when PV characteristics exhibit multiple local peaks due to irregular shading. The proposed IPSO algorithm improves on PSO by adding an "iterative best" value and adapting the cognitive and social coefficients over time, allowing it to more effectively track the global maximum power point under complex shading scenarios. Simulation results show the IPSO method converges faster than conventional PSO and achieves higher tracking efficiency under varying irradiance conditions defined by industry standards.
This document presents a model for a photovoltaic (PV) array with maximum power point tracking (MPPT) using a boost converter. It describes:
1) Modelling of PV cell, boost converter, and inverter components in MATLAB Simulink.
2) Development of an averaged model for a PV array connected to a boost converter with MPPT control using the incremental conductance algorithm.
3) Simulation results showing the model operating at maximum power point under changing irradiance conditions and having higher efficiency compared to without MPPT control.
Modelling of PV Array with MPP Tracking & Boost DC-DC ConverterIOSR Journals
This document presents a model for a photovoltaic (PV) array with maximum power point tracking (MPPT) using a boost converter. It describes:
1) Modelling of PV cell, boost converter, and inverter components in MATLAB Simulink.
2) Development of an averaged model for a PV array connected to a boost converter with MPPT control using the incremental conductance algorithm.
3) Simulation results showing the model operating at maximum power point under changing irradiance conditions and having higher efficiency compared to without MPPT control.
Several algorithms have been offered to track the Maximum Power Point when we have one maximum power point. Moreover, fuzzy control and neural was utilized to track the Maximum Power Point when we have multi-peaks power points. In this paper, we will propose an improved Maximum Power Point tracking method for the photovoltaic system utilizing a modified PSO algorithm. The main advantage of the method is the decreasing of the steady state oscillation (to practically zero) once the Maximum Power Point is located. moreover, the proposed method has the ability to track the Maximum Power Point for the extreme environmental condition that cause the presence of maximum multi-power points, for example, partial shading condition and large fluctuations of insolation. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging circumstance, namely step changes in irradiance, step changes in load, and partial shading of the Photovoltaic array. Finally, its performance is compared with the perturbation and observation” and fuzzy logic results for the single peak, and the neural-fuzzy control results for the multi-peaks.
A Comprehensive Analysis of Partial Shading Effect on Output Parameters of a ...IJECEIAES
One of the issues of grid-connected photovoltaic systems is the effect of the partial shading on the key parameters and performance of the system. In practice, a share of the entire PV panel may shadded because of the various reasons, inevitably. In this case, the key parameters of the system output are affected with respect to the shading extent and paradigm. In this paper, the effects of the various partial shading patterns on the ouput of the system are examined. This is performed by deriving relevant equations and appropriate modeling of the system and defining different scenarios. The analysis on the system performance is carried out on the dominant output parameters including panel voltage, panel power, and total harmonic distortion (THD) of the inverter. Also, the study considers the effect of using bypass diodes in the panels or not. Addintionally, to compare derived conclusions, the study is implementd on a practical system. The set up is made up of a 7-level multilevel inverter, a Z-source converter, and 1 kW lateral circuitry. The real world test results of the study demonstrate a negligible deviation compared to the simulation results.
Design and investigations of MPPT strategies for a wind energy conversion sys...IJECEIAES
The purpose of this work is to design and to discuss various strategies to optimize the production of a wind energy conversion chain based on the doubly fed induction generator (DFIG), by capturing the maximum power at the wind turbine, using maximum power point tracking (MPPT) and pitch control. The proposed controls allow the generator to monitor the optimal operating points of the turbines regardless of wind speed variations, system parameters disturbance, and parameters variation. Simulation of WECS based on a 1.5 MW wound rotor induction generator under MATLAB/SIMULINK is carried out using the PI controller (PIC), RST controller and fuzzy logic controller (FLC). Analysis and comparisons are made for different operating scenarios: Reference tracking, robustness under variable wind speed conditions and parameters variation. The application of FLC provides a very interesting outcome for the robustness and the dynamic challenges.
Investigation of Interleaved Boost Converter with Voltage multiplier for PV w...ecij
This paper depicts the significance of Interleaved Boost Converter (IBC) with diode-capacitor multiplierwith PV as the input source. Maximum Power Point Tracking (MPPT) was used to obtain maximum power from the PV system. In this, interleaving topology is used to reduce the input current ripple, output voltage ripple, power loss and to suppress the ripple in battery current in the case of Plugin Hybrid Electric Vehicle (PHEV). Moreover, voltage multiplier cells are added in the IBC configuration to reduce the narrow turn-off periods. Two MPPT techniques are compared in this paper: i) Perturb and Observe (P&O) algorithm ii) Fuzzy Logic . The two algorithms are simulated using MATLAB and the comparison of performance parameters like the ripple is done and the results are verified.
Tunicate swarm algorithm based maximum power point tracking for photovoltaic...IJECEIAES
A new maximum power point tracking (MPPT) technique based on the bioinspired metaheuristic algorithm for photovoltaic system (PV system) is proposed, namely tunicate swarm algorithm-based MPPT (TSA-MPPT). The proposed algorithm is implemented on the PV system with five PV modules arranged in series and integrated with DC-DC buck converter. Then, the PV system is tested in a simulation using PowerSim (PSIM) software. TSA-MPPT is tested under varying irradiation conditions both uniform irradiation and non-uniform irradiation. Furthermore, to evaluate the performance, TSA-MPPT is compared with perturb & observe-based MPPT (P&O-MPPT) and particle swarm optimization-based MPPT (PSO-MPPT). The TSA-MPPT has an accuracy of 99% and has a reasonably practical capability compared to the MPPT technique, which already existed before.
Performance enhancement of maximum power point tracking for grid-connected ph...TELKOMNIKA JOURNAL
This paper presents a new variant of smart adaptive algorithm of Maximum Power Point Tracking (MPPT) in the photovoltaic (PV) system. The algorithm was adopted from Modified Perturb and Observe (MP&O). The smart adaptive MPPT is used to search Maximum Power Point (MPP) of the PV system under various irradiance changes. This algorithm incorporates information of current change (ΔI), maximum operating point margin and dynamic perturbation step to prevent MPPT diverging away from the MPP and minimize the steady state oscillation. The smart adaptive MPPT algorithm performance is compared with the dI-P&O and conventional P&O to prove its effectiveness. The comparison is performed under the various gradient of irradiance change. It was found that, for all the tests, the smart adaptive algorithm scheme improve the tracking efficiency under various gradients of irradiance changes and increase the efficiency of extraction power from PV system.
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
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%.
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.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
2. ISSN: 2088-8708
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significant techniques. Parlak and Can [8] proposed an improved global scanning method which tracks
MPPT more accuratelybut consumes a long time in the optimization process leading to power loss. The work
of Zhang and Cao [9] has used Fibonacci MPP optimization mechanism which can be adapted for the change
in climatic condition, but it also consumes more convergence time. A unique work of Ramaprapha et al. [10]
has used the PSO algorithm to select therandom position of particles as an initial value of particles. Also,
many other types of research like Shi et al. [11], Gowaid et al. [12], Cheng et al. [13], Sing et al. [14], Yaichi
et al. [15], etc. have addressed the issues in MPPT with different approaches. The behavior of the solar WSN
under different climatic condition is discussed in Hamili et al. [16]. The work of El malah et al. [17] have
discussed a power control mechanisms to reduce the cost and yielding higher performance. In a work of
Samosir et al. [18] the fuzzy logic based simulation model is presented to get MPPT for PV application.
Thus, in this manuscript, a modified PSO algorithm for MPPT in the PV array is presented to overcome
the recent research issue and bring more effectiveness in MPPT. The manuscript is categorized as, section 1
discussing the background of PV circuit, consideration of PSO in MPPT. Section 2 gives research problem,
section 3 explains proposed modified PSO algorithm along with algorithm description and implementation,
section 4 illustrates the results analysis and section 5 gives the conclusion of the proposed PSO algorithm.
a. The background
From the existing researches of Ishaque et al. [19, 20], Yang et al. [21], Chunhua et al. [22],
Dongras et al. [23], found that the PV model with two bypass diodes offers a higher degree of accuracy.
The same concept is adapted in designing the PV circuit and has been presented in Figure 1.
Figure 1. (a) PV circuit, (b) equivalent circuit
The equivalent model of PV array is given in Figure 1(b), where the output current (I) can be
obtained as,
r
r
t
r
t
r
p
P
IPV
V
ISV
I
V
ISV
III
)(
1
)(
exp1
)(
exp
22
2
11
1
(1)
where pI is PV current, 1I is current across D1, 2I is current across D2,
21 & : Ideal constant variables of two diodes
rr PS & : Serial (<1KΩ) and parallel (>1KΩ) resistors respectively
21 & tt VV : Thermal voltages having a number of PV cells (Ns)
q
kTNs
VV tt
21
where,
k : Pohl Seidman constant (1.381x10-23
J/k)
q : Charge constant (1.602x10-19
C)
T : Absolute temperature of PV cell.
3. Int J Elec & Comp Eng ISSN: 2088-8708
A modified particle swarm optimization (PSO) algorithm to … (Yoganandini A. P.)
5003
The simplified form of the PV module is given in Figure 1(b), and the output current can be taken as,
r
r
t
r
t
r
p
P
ISV
Vp
IPV
V
ISV
III
)(
2
)1(
)(
exp
)(
exp1
(2)
For large PV based power generation system, the design of Pv is preferred in Figure 1(b). It contains
the series of PV modules (Nss) or parallel of PV modules (Npp). The matrix form of PV design can be
represented as, [ ppss NN ]. Further, to enhance the structure of a series/parallel circuit, the (2) can be
modified as,
r
r
sst
r
tss
r
ppp
P
ISV
NVp
IPV
VN
ISV
IINI
)(
)1(
)(
exp
)(
exp1
(3)
where,
pp
ss
N
N
b. PSO in MPPT
The algorithm of Particle Swarm Optimization (PSO) is presented by Kennedy and Eberhart [20].
This is a significant method which can be used for multimodal function optimization and swarm optimization
search guide generated from competition and cooperation among the particles in swarm. To illustrate
the PSO algorithm for MPPT controller, the solution vector (
k
ix ) can be defined.
],...,,,[ 321 jj
k
i dddddx (4)
where, jd is particle duty ratio=1, 2, 3… pN
The objective function for this duty ratio can be calculated as,
1
)()(
k
i
k
i dPdP (5)
The property of PSO is that it adds three duty cycles 321 ,, ddd and forwards then to the power
converters to initialize the optimization process. The following Figure 2 gives the movement of particles
in search of MPP at different iterations. The triangles represent the duty cycles. In the first iteration as shown
in Figure 2(a) of the particle movement, the duty cycles are personal best (Pb) while d2 is global best (Gb)
and is the optimal value of PV array.
The movement of particles in the second iteration as shown in Figure 2(b). In this, because of Gb,
which is offering optimal value of power (5), the velocity, Pb(di) is zero, and the factor Gb (d2) is zero.
Hence, the velocity of the Gb particle (d2) is zero, which leads to zero speed and unchanged duty ratio.
Thus, in search optimization, the particles do not have any effect. In order to utilize this situation,
some disturbance will be added, and it assures the change in optimal value. The movement of particles in
the third iteration is presented in Figure 2(c). In first two iterations yield better fitness, speed, and the particle
direction is unchanged. Hence, they stay in the same direction along Gb. In the third iteration, all the duty
cycles 321 ,, ddd stay at low speed for MPP. At this speed, the duty ratio will be constant, and the system
will occupy a stable operating point, which helps in minimizing the oscillations of MPP. If the PV array is in
partial shade, the P-V curve faces multi-peak state P1, P2, and P4 local poles while P3 global poles (obtained
from 4th
iteration as shown in Figure 2(d). The output of the system with duty cycles 321 ,, ddd where Pb is
particles, the global peak (P3) is obtained and optimization is initialized at initial duty ratio (Pb, i).
c. Research problem
The work of Yoganandini and Anita [24] have provided an insight into MPPT techniques in PV
modules by dealing with existing researches, research gap, and offered a futuristic idea for the research
community. From the recent research survey, it is observed that at the slow change in optimal radiations,
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the PSO need to provide an appropriate value of duty cycle. During MPP tracking, changes in air ratio,
initialization, and variation in duty ratio, range of the particles in PV-curve increases. Hence, large
fluctuations may appear in providing optimal search solution. This yields high computational cost and energy
wastage. Another problem which needs to be considered is that tracking of MPP must be fast enough to track
speed but, the duty ratio and volatility are not feasible in the PSO algorithm, and it does not yield proper
tracking of MPP. Also, change in the intensity of solar radiation, and it leads to variation in operating point.
In this scenario, small changes in duty cycle may lead to slower search in MPP. This is more critical during
shadow condition. Hence, the empty ratio is not used to search the PV curve in a large area where the traced
MPP may be local peak than the global peak. Thus, there is a need for the modified algorithm to overcome
the above-stated problem.Hence, the problem statement is “to introduce a modified PSO algorithm to
enhance the performance of MPPT from PV array."
1st
iteration 2nd
iteration
3rd
iteration 4th
iteration
Figure 2. Movement of particles in different iterations
2. MODIFIED PSO ALGORITHM FOR MPPT
The previous work of Yoganandini and Anita [25] have presented a cost-effective MPPT technique
for MPPT, where computational time is reduced and achieved cost optimization. This manuscript aims to
enhance the performance of the MPP by introducing the modified PSO for the PV array. In the proposed
algorithm, the duty cycle is partitioned into two parts. The previous duty ratio exhibits the factor of
linearization (K1) increases or decreases the ratio based on PV array output. Similarly, in providing new PV
curve for MPP by using search optimization, two duty cycles d1 and d3 in the positive and negative direction
to K2 constant value perturbation. The following Figure 3 provides an estimation model for K1 where it can
be observed that the maximum power of array and respective power (pMPP), duty ratio, the relationship
among Pb, Gb to DC/DC converter having pMPPd with respect to duty ratio. The response optimized
1 can be minimized to 0.1, step 0.1. However, there exist two expressions are considered, which brings
the relationship between dbest and pMPP. Also, there exists a linear relationship between array power and duty.
MPPMPPoldoldnew PP
K
dd ,
1
1
oldd is a previous duty ratio for Gb. The slope ( 1K )
d
pMMP
for linear relation changes as per change in
5. Int J Elec & Comp Eng ISSN: 2088-8708
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5005
operating power and its value is almost equal to the new optimal duty cycle. Hence, the initialization of duty
ratio must perform the searching of the P-V curve and will quickly do the tracking of new MPP.
Figure 3. Relation among Gb, duty cycle and pMPP
From the above analysis, it has been found that, the reduction in solar radiation (from wavelength
1 1.0 ) always leads to load line in PV array I-V gives maximum MPP voltage (VMPP) to the right
of plot curve. The increment in sunshine brings load line to the right. The difference between VMPP and
output voltage will become small, and it leads to a small variation in power.Hence, the same value of dold&
K1 is not to be deleted. Thus, the PSO algorithm needs to have more iteration to track MPP.
To neutralize such type of problems, a simple assumption is made with two different values of K1.
i.e.,
0
2
0
1
1
1
Pif
K
PifK
K
In this equation, oldPPP
The value of P>0 & P<0 indicates the decrement and increment in sunshine radiation. In order to get
the duty ratio of new perturbation for d1 and d3 respectively. The following formula of data ratio updates
position, and negative direction.
)(,),()( 33221 KddKdd newi Where 05.02 K
The selection mechanism of this 0.05 helps to manage low power fluctuation but, during the partial shade,
the working voltage may increases up to 85%, this helps PSO algorithm to track global peak more.
The following section gives the algorithm implementation.
Algorithm of modified PSO
Input: Ip, Vp
Output: Gb and Pb
Start
Step-1: initialize & detect Ip and Vp
Step-2: Compute ppi IVP )(
Step-3: Check if 0;0 PP
Compute (d) at Vi=(1,2,3)=0, Np=3, K=0
Else increment i=i+1;
Check i>Np
Increment k=k+1;
If K=1;
Pbest=di
Else check i=1;
Step-4: Compute Pb&Gb
Step-5: Update disturbances
End
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The algorithm is initialized by detecting the PV current (Ip) and PV voltage (Vp) (Step-1).
Further, the initial power of the PV cell is computed by using the general formula of power ppi IVP )(
(Step-2). Later, the condition of P>0 or P<0 is verified (Step-3). If the condition is satisfied, the duty ratio (d)
is calculated at duty cycles (1,2,3) of voltage Vi(1,2,2)=0 number of particles (Np)=3, constant k=0. If it is
not satisfied, then the number of iterations will be incremented by 1. Further, it is checked for "i>Np" and if
it is satisfied, then ‘k' value is incremented by 1. If k=1, the "Pb" value will be duty cycle (di) i.e., Pb=di.
Similarly, if ‘k=1' is not satisfied, it will be checked for i=1. Then, the value of Pb can be computed after
varying the condition P(i)>P(i-1). In case, the condition is satisfied, then “Pb=di” else “Pb=d(i-1). ”.
Similarly, to calculate global best (Gb) same procedure of incrementing (i=i+1) and checking "i>Np." Based
on this condition, Gb is computed as,
)max( bb PG
Finally, the disturbance among Pb, output voltage, and Gb is updated. The duty cycle of disturbance
is computed by using previous duty ratio di(k) and local Pb. The difference between ‘i’ and previous di(k)
and Gb. Hence, the power converter and tracking best Pb, Gb and I are possible in the proposed algorithm.
The significance of proposed PSO is that it yields faster search and tracks the MPP optimal solution.
After acquiring the MPP by particles, the velocity almost becomes zero. Hence, no oscillations will be
observed in steady state. The steady state oscillation is necessary as it is helpful in getting the efficiency of
MPPT. Another significant feature of modified PSO is that it exhibits 3-duty cycles, and hence, it does not
lose direction in short term fluctuations. The proposed PSO effectively able to track the global peak.
3. RESULT ANALYSIS
The proposed system model is simulated using MATLAB. In the optimization process, the fitness
value is updated by PV array output power. The performance analysis of the modified PSO is done with
traditional PSO under partial shading condition aiming with accurate MPP tracking. Figure 4 represents
the tracking result of traditional PSO, where it is observed that a large range of fluctuations exists in
optimization. This misjudges the MPP and takes ~0.045sec for tracking the MPP.
Figure 4. Tracking of MPP with traditional PSO
The proposed, modified PSO considered search-based optimization uses 3-duty cycles and does not
lose its direction in short term fluctuation. Figure 5 shows the power Vs. time curve obtained from proposed
PSO is smoother than traditional PSO, and it takes only ~0.038sec for MPP tracking, which is improved
about 0.08secs. Hence, the modified PSO makes the process more stable and improves the MPP
performance.The proposed PSO improved the dynamic response speed tracking accuracy in a steady state.
7. Int J Elec & Comp Eng ISSN: 2088-8708
A modified particle swarm optimization (PSO) algorithm to … (Yoganandini A. P.)
5007
.
Figure 5. Tracking of MPP with proposed PSO
4. CONCLUSION
In this research, the proposed MPP tracking system is aimed to enhance the tracking accuracy and
speed. The proposed system introduced an MPPT technique based on the modified PSO algorithms which
bring high efficiency. The search based method is considered with 3-duty cycles and does not lose its
direction in short term fluctuation in steady state. Another significance is that the proposed PSO has taken
less time (0.038secs) to track the MPP than traditional MPP (0.045sec) found improvement of 0.008secs.
This gives that the performance of the MPPT is enhanced with an efficiency of 99%. The scope of
the proposed study is that it can be considered with other machine learning approaches under different
environmental condition. Further, the research can be carried out with different types of PV arrays.
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BIOGRAPHIES OF AUTHORS
Yoganandini A. P., she has studied BE in Electrical & Electronics Engineering from STJIT,
Karnataka university, Dharwad. M.Tech in computer applications in industrial drives from M. S.
Ramaiah Institute of Technology, Bangalore from Visveswaraiah Technological university,
Belgaum, Karnataka, India. Pursing PhD in area of Photovoltaic module. Published eight papers in
International journals. Five papers in national conferences Attended international conference
conducted at Dubai, UAE, got best paper award in year 2015 March. Currently working as an
Assistant Professor in Sambharam Institute of Technology, Bangalore, Karnataka, India. Having
12 years of teaching experience.
Dr. Anitha., she has studied BE in electrical & Electrical engineering and M.Tech in power
system Engineering from UVCE, Karnataka, India, PhD in Renewable energy source from
Avinasamlingum University, koyamattur, Tamilnau, India Published Fifteen international Journals
and Ten national journals and Attended Ten international conference. Having 34 years of teaching
experience, currently working as an Associate Professor in Electrical and Electronics Department
R.V college of Engineering, Bangalore, Karnataka, India.