This paper is an attempt to develop a multi-facts device placementin deregulated power system using optimization algorithms. The deregulated power system is the recent need in the power distribution as it has many independent sellers and buyers of electricity. The problem of deregulation is the quality of the power distribution as many sellers are involved. The placement of FACTS devices provides the solution for the above problem. There are researches available for multiple FACTS devices. The optimization algorithms like Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) are implemented to place the multiple FACTS devices in a power system. MATLAB based implementation is carried out for applying Optimal Power Flow (OPF) with variation in the bus power and the line reactance parameters. The cost function is used as the objective function. The cost reduction of FACTS as well as generation by placement of different compensators like, Static Var Compensator (SVC), Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC). The cost calculation is done on the 3-seller scenario. The IEEE 14 bus is taken here as 3-seller system.
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 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
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 paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
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
Reduction of Active Power Loss byUsing Adaptive Cat Swarm Optimizationijeei-iaes
This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely ‘Adaptive Cat Swarm Optimization’ (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. The projected ACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the high-quality performance of the planned algorithm in tumbling the real power loss.
Multi-objective whale optimization based minimization of loss, maximization o...IJECEIAES
Huge need in electricity causes placement of Distribution Generation (DG)s like Photovoltaics (PV) systems in distribution side for enhancing the loadability by improving the voltage stability and minimization of loss with minimum cost. Many optimal placements of DG have done in focus of minimum loss and improving voltage profile. This Whale optimization is a new optimization technique framed with mathematics of spiral bubble-net feeding behavior of humpback whales for solving a power system multi-objective problem considering cost of the power tariff and DG. Here main objectives are minimizing loss and cost with maximization of voltage stability index. IEEE 69 power system data is used for solution of the proposed method.
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 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
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 paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
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
Reduction of Active Power Loss byUsing Adaptive Cat Swarm Optimizationijeei-iaes
This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely ‘Adaptive Cat Swarm Optimization’ (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. The projected ACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the high-quality performance of the planned algorithm in tumbling the real power loss.
Multi-objective whale optimization based minimization of loss, maximization o...IJECEIAES
Huge need in electricity causes placement of Distribution Generation (DG)s like Photovoltaics (PV) systems in distribution side for enhancing the loadability by improving the voltage stability and minimization of loss with minimum cost. Many optimal placements of DG have done in focus of minimum loss and improving voltage profile. This Whale optimization is a new optimization technique framed with mathematics of spiral bubble-net feeding behavior of humpback whales for solving a power system multi-objective problem considering cost of the power tariff and DG. Here main objectives are minimizing loss and cost with maximization of voltage stability index. IEEE 69 power system data is used for solution of the proposed method.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Oscillatory Stability Prediction Using PSO Based Synchronizing and Damping To...journalBEEI
This paper presents the assessment of stability domains for the angle stability condition of the power system using Particle Swarm Optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ksand damping torque coefficients Kd to identify the angle stability condition on multi-machine system. In order to accelerate the determination of angle stability, PSO is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as Evolutionary Programming (EP) and Artificial Immune System (AIS). Validation with respect to eigenvalues determination, Least Square (LS) method and minimum damping ratio ξmin confirmed that the proposed technique is feasible to solve the angle stability problems.
Multi objective-optimization-with-fuzzy-based-ranking-for-tcsc-supplementary-...Cemal Ardil
This document summarizes a research paper that investigates using multi-objective optimization and genetic algorithms to design a thyristor controlled series compensator (TCSC) controller to improve both rotor angle stability and voltage stability in a power system. The researchers formulate the controller design as a multi-objective optimization problem with the objectives of improving oscillatory stability and voltage profile. A genetic algorithm is used to generate a Pareto set of optimal solutions. A fuzzy-based method is then employed to select the best compromise solution from the Pareto set. Simulation results demonstrate the effectiveness of the proposed approach.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A chaotic particle swarm optimization (cpso) algorithm for solving optimal re...Alexander Decker
This document presents a chaotic particle swarm optimization (CPSO) algorithm for solving the multi-objective reactive power dispatch problem. The CPSO algorithm aims to avoid premature convergence by fusing ergodic and stochastic chaos. It formulates reactive power dispatch as an optimization problem with two objectives: minimizing real power losses and maximizing static voltage stability margin. The CPSO is tested on the IEEE 30 bus system and is shown to reduce power losses and maximize voltage stability more than other algorithms.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
Multi-Objective Aspects of Distribution Network Volt-VAr OptimizationPower System Operation
This document discusses multi-objective optimization approaches for distribution network volt-var optimization (VVO). It presents two common multi-objective optimization techniques: the e-constraint method and weighted-sum method. The e-constraint method optimizes one objective function while setting the other objectives as constraints. The weighted-sum method assigns weighting coefficients to each objective and minimizes their sum. The document demonstrates these methods on a test distribution feeder with controllable capacitor banks and a solar farm, seeking to optimize both active and reactive power.
5. 9375 11036-1-sm-1 20 apr 18 mar 16oct2017 ed iqbal qcIAESIJEECS
In this paper, Tailored Flower Pollination (TFP) algorithm is proposed to solve the optimal reactive power problem. Comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight, the performance of the flower pollination algorithm has been improved. Proposed TFP algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Optimization of Automatic Voltage Regulator Using Genetic Algorithm Applying ...IJERA Editor
In an interconnected power system, if a load demand changes randomly, both frequency and tie line power
varies. The main aim of automatic voltage controller is to minimize the transient variations in these variables
and also to make sure that their steady state errors is zero. Many modern control techniques are used to
implement a reliable controller. The objective of these control techniques is to produce and deliver power
reliably by maintaining voltage within permissible range. When real power changes, system frequency gets
affected while reactive power is dependent on variation in voltage value. That’s why real and reactive power is
controlled separately. Our objective is here for to study and analyze the Genetic algorithms and their
application to the problems of Function Optimization and System Identification. Since there are other methods
traditionally adopted to obtain the optimum value of a function (which are usually derivative based), the project
aims at establishing the superiority of Genetic Algorithms in optimizing complex, multivariable and multimodal
function. The Genetic Algorithm is a popular optimization technique which is bio-inspired and is based on the
concepts of natural genetics and natural selection theories proposed by Charles Darwin.
Model Order Reduction of an ISLANDED MICROGRID using Single Perturbation, Dir...IRJET Journal
This document discusses using model order reduction techniques to simplify the model of an islanded microgrid system from 6th order to lower order approximations. It evaluates three methods: single perturbation, direct truncation, and particle swarm optimization. Single perturbation and direct truncation are used to reduce the model to 4th order, while particle swarm optimization further reduces it to 2nd order. The responses of the reduced models are compared to the original 6th order model, showing that even the 2nd order model reduced using particle swarm optimization provides an improved response.
Economic and Emission Dispatch using Whale Optimization Algorithm (WOA) IJECEIAES
This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using the above-mentioned algorithm have been compared with obtained results using other intelligent methods such as particle swarm Optimization. The overall performance of this algorithm collates with early proven optimization methodology, Particle Swarm Optimization (PSO). The minimum cost for the generation of units is obtained for the standard bus system.
Alienor method applied to induction machine parameters identification IJECEIAES
This paper presents an identification method to estimate simultaneously the electrical and mechanical induction machine (IM) parameters by using only the measured current and the corresponding phase voltage. This identification method is based on the output error and uses the multidimensional Alienor global optimization method as a minimization technique. Alienor method is essentially based on converting multivariable problem to monovariable one. To improve the Alienor method performance, the reducing transformation is proposed and compared with the genetic algorithm (GA). Firstly, the identification method is verified using the simulated data. Secondly, the validation is then confirmed by measured data from one machine. The corresponding computed transient and steady state currents agree well with the measured data. The results obtained show the superiority of the proposed Alienor method versus GA in terms of computing time.
The document summarizes research on thinning semi-elliptical and quarter-elliptical antenna arrays using genetic algorithm optimization to reduce side-lobe levels. Key points:
1) Genetic algorithms were used to optimize thinning of semi-elliptical and quarter-elliptical arrays with uniform excitation and spacing to produce narrow beams without degrading performance.
2) Simulation results showed thinning using genetic algorithms reduced side-lobe levels of the arrays compared to fully populated arrays.
3) Maximum side-lobe levels, beamwidths, and other metrics were tabulated for various array sizes and geometries, indicating thinning effectively lowered side-lobes.
Economic Load Dispatch Problem with Valve – Point Effect Using a Binary Bat A...IDES Editor
This paper proposes application of BAT algorithm
for solving economic load dispatch problem. BAT
algorithmic rule is predicated on the localization
characteristics of micro bats. The proposed approach has
been examined and tested with the numerical results of
economic load dispatch problems with three and five
generating units with valve - point loading without
considering prohibited operating zones and ramp rate limits.
The results of the projected BAT formula are compared with
that of other techniques such as lambda iteration, GA, PSO,
APSO, EP, ABC and basic principle. For each case, the
projected algorithmic program outperforms the answer
reported for the existing algorithms. Additionally, the
promising results show the hardness, quick convergence
and potency of the projected technique.
Daily Peak Load Forecast Using Artificial Neural NetworkIJECEIAES
The document describes a study that used an artificial neural network model to forecast daily peak electrical loads. The network was trained using historical daily peak load and temperature data from Bangalore, India. It had 24 input nodes for the load and temperature data, a single hidden layer with varying numbers of nodes, and 12 output nodes to forecast the daily peak load for each month. The optimal network structure was found to have 21 nodes in the hidden layer. When trained and tested on load data from March 2017, it produced forecasts with errors less than 2% compared to over 10% for conventional linear and polynomial fitting methods. The neural network approach was thus found to provide more accurate short-term load forecasting.
Khaled eskaf presentation predicting power consumption using genetic algorithmKhaled Eskaf
The document discusses predicting short-term electrical energy consumption using a dynamic model and genetic algorithm. It proposes extracting features from historical energy consumption time series data using a dynamic system model to determine impulse forces and damping factors. A genetic algorithm is then used to predict future consumption values based on these features, with the ability to continuously learn from new data online. The approach is evaluated using root-mean-square error and shown to achieve accurate predictions between 5x10-5 to 1x10-4 kilowatt hours.
This document summarizes research on analyzing the steady-state performance of a self-excited induction generator using three optimization techniques: genetic algorithms, pattern search, and quasi-Newton methods. It provides background on induction generators and how they can operate as self-excited generators by connecting capacitors to the stator terminals. The document presents the standard steady-state equivalent circuit model and derives nonlinear equations that are solved using the three optimization techniques to determine unknown parameters. The performance of the self-excited induction generator is then evaluated based on the determined parameters.
Dwindling of real power loss by using Improved Bees Algorithmpaperpublications3
Abstract: In this paper, a new Improved Bees Algorithm (IBA) is proposed for solving reactive power dispatch problem. The aim of this paper is to utilize an optimization algorithm called the improved Bees Algorithm, inspired from the natural foraging behaviour of honey bees, to solve the reactive power dispatch problem. The IBA algorithm executes both an exploitative neighbourhood search combined with arbitrary explorative search. The proposed Improved Imperialist Competitive Algorithm (IBA) algorithm has been tested on standard IEEE 57 bus test system and simulation results show clearly the high-quality performance of the projected algorithm in reducing the real power loss.
Keywords: Optimal Reactive Power, Transmission loss, honey bee, foraging behaviour, waggle dance, bee’s algorithm, swarm intelligence, swarm-based optimization, adaptive neighbourhood search, site abandonment, random search.
Optimization of the Thyristor Controlled Phase Shifting Transformer Using PSO...IJECEIAES
This document summarizes an article that investigates optimizing the placement and sizing of a Thyristor Controlled Phase Shifting Transformer (TCPST) and Thyristor Controlled Series Capacitor (TCSC) combination on a 30-bus power system using a Particle Swarm Optimization (PSO) algorithm. The PSO algorithm was used to determine the optimal location and ratings of the TCPST-TCSC devices to minimize power losses. Implementing the optimized TCPST-TCSC combination resulted in a 46.47% reduction in power losses, outperforming the use of capacitor banks alone which achieved a 42.03% reduction. The TCPST-TCSC solution found with PSO also performed
Enhancing radial distribution system performance by optimal placement of DST...IJECEIAES
In this paper, A novel modified optimization method was used to find the optimal location and size for placing distribution Static Compensator in the radial distribution test feeder in order to improve its performance by minimizing the total power losses of the test feeder, enhancing the voltage profile and reducing the costs. The modified grey wolf optimization algorithm is used for the first time to solve this kind of optimization problem. An objective function was developed to study the radial distribution system included total power loss of the system and costs due to power loss in system. The proposed method is applied to two different test distribution feeders (33 bus and 69 bus test systems) using different Dstatcom sizes and the acquired results were analyzed and compared to other recent optimization methods applied to the same test feeders to ensure the effectiveness of the used method and its superiority over other recent optimization mehods. The major findings from obtained results that the applied technique found the most minimized total power loss in system, the best improved voltage profile and most reduction in costs due power loss compared to other methods.
Coordinated Placement and Setting of FACTS in Electrical Network based on Kal...IJECEIAES
To aid the decision maker, the optimal placement of FACTS in the electrical network is performed through very specific criteria. In this paper, a useful approach is followed; it is based particularly on the use of KalaiSmorodinsky bargaining solution for choosing the best compromise between the different objectives commonly posed to the network manager such as the cost of production, total transmission losses (Tloss), and voltage stability index (Lindex). In the case of many possible solutions, Voltage Profile Quality is added to select the best one. This approach has offered a balanced solution and has proven its effectiveness in finding the best placement and setting of two types of FACTS namely Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) in the power system. The test case under investigation is IEEE-14 bus system which has been simulated in MATLAB Environment.
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Oscillatory Stability Prediction Using PSO Based Synchronizing and Damping To...journalBEEI
This paper presents the assessment of stability domains for the angle stability condition of the power system using Particle Swarm Optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ksand damping torque coefficients Kd to identify the angle stability condition on multi-machine system. In order to accelerate the determination of angle stability, PSO is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as Evolutionary Programming (EP) and Artificial Immune System (AIS). Validation with respect to eigenvalues determination, Least Square (LS) method and minimum damping ratio ξmin confirmed that the proposed technique is feasible to solve the angle stability problems.
Multi objective-optimization-with-fuzzy-based-ranking-for-tcsc-supplementary-...Cemal Ardil
This document summarizes a research paper that investigates using multi-objective optimization and genetic algorithms to design a thyristor controlled series compensator (TCSC) controller to improve both rotor angle stability and voltage stability in a power system. The researchers formulate the controller design as a multi-objective optimization problem with the objectives of improving oscillatory stability and voltage profile. A genetic algorithm is used to generate a Pareto set of optimal solutions. A fuzzy-based method is then employed to select the best compromise solution from the Pareto set. Simulation results demonstrate the effectiveness of the proposed approach.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A chaotic particle swarm optimization (cpso) algorithm for solving optimal re...Alexander Decker
This document presents a chaotic particle swarm optimization (CPSO) algorithm for solving the multi-objective reactive power dispatch problem. The CPSO algorithm aims to avoid premature convergence by fusing ergodic and stochastic chaos. It formulates reactive power dispatch as an optimization problem with two objectives: minimizing real power losses and maximizing static voltage stability margin. The CPSO is tested on the IEEE 30 bus system and is shown to reduce power losses and maximize voltage stability more than other algorithms.
Economic Dispatch using Quantum Evolutionary Algorithm in Electrical Power S...IJECEIAES
Unpredictable increase in power demands will overload the supply subsystems and insufficiently powered systems will suffer from instabilities, in which voltages drop below acceptable levels. Additional power sources are needed to satisfy the demand. Small capacity distributed generators (DGs) serve for this purpose well. One advantage of DGs is that they can be installed close to loads, so as to minimise loses. Optimum placements and sizing of DGs are critical to increase system voltages and to reduce loses. This will finally increase the overall system efficiency. This work exploits Quantum Evolutionary Algorithm (QEA) for the placements and sizing. This optimisation targets the cheapest generation cost. Quantum Evolutionary Algorithm is an Evolutionary Algorithm running on quantum computing, which works based on qubits and states superposition of quantum mechanics. Evolutionary algorithm with qubit representation has a better characteristic of diversity than classical approaches, since it can represent superposition of states.
Multi-Objective Aspects of Distribution Network Volt-VAr OptimizationPower System Operation
This document discusses multi-objective optimization approaches for distribution network volt-var optimization (VVO). It presents two common multi-objective optimization techniques: the e-constraint method and weighted-sum method. The e-constraint method optimizes one objective function while setting the other objectives as constraints. The weighted-sum method assigns weighting coefficients to each objective and minimizes their sum. The document demonstrates these methods on a test distribution feeder with controllable capacitor banks and a solar farm, seeking to optimize both active and reactive power.
5. 9375 11036-1-sm-1 20 apr 18 mar 16oct2017 ed iqbal qcIAESIJEECS
In this paper, Tailored Flower Pollination (TFP) algorithm is proposed to solve the optimal reactive power problem. Comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight, the performance of the flower pollination algorithm has been improved. Proposed TFP algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Optimization of Automatic Voltage Regulator Using Genetic Algorithm Applying ...IJERA Editor
In an interconnected power system, if a load demand changes randomly, both frequency and tie line power
varies. The main aim of automatic voltage controller is to minimize the transient variations in these variables
and also to make sure that their steady state errors is zero. Many modern control techniques are used to
implement a reliable controller. The objective of these control techniques is to produce and deliver power
reliably by maintaining voltage within permissible range. When real power changes, system frequency gets
affected while reactive power is dependent on variation in voltage value. That’s why real and reactive power is
controlled separately. Our objective is here for to study and analyze the Genetic algorithms and their
application to the problems of Function Optimization and System Identification. Since there are other methods
traditionally adopted to obtain the optimum value of a function (which are usually derivative based), the project
aims at establishing the superiority of Genetic Algorithms in optimizing complex, multivariable and multimodal
function. The Genetic Algorithm is a popular optimization technique which is bio-inspired and is based on the
concepts of natural genetics and natural selection theories proposed by Charles Darwin.
Model Order Reduction of an ISLANDED MICROGRID using Single Perturbation, Dir...IRJET Journal
This document discusses using model order reduction techniques to simplify the model of an islanded microgrid system from 6th order to lower order approximations. It evaluates three methods: single perturbation, direct truncation, and particle swarm optimization. Single perturbation and direct truncation are used to reduce the model to 4th order, while particle swarm optimization further reduces it to 2nd order. The responses of the reduced models are compared to the original 6th order model, showing that even the 2nd order model reduced using particle swarm optimization provides an improved response.
Economic and Emission Dispatch using Whale Optimization Algorithm (WOA) IJECEIAES
This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using the above-mentioned algorithm have been compared with obtained results using other intelligent methods such as particle swarm Optimization. The overall performance of this algorithm collates with early proven optimization methodology, Particle Swarm Optimization (PSO). The minimum cost for the generation of units is obtained for the standard bus system.
Alienor method applied to induction machine parameters identification IJECEIAES
This paper presents an identification method to estimate simultaneously the electrical and mechanical induction machine (IM) parameters by using only the measured current and the corresponding phase voltage. This identification method is based on the output error and uses the multidimensional Alienor global optimization method as a minimization technique. Alienor method is essentially based on converting multivariable problem to monovariable one. To improve the Alienor method performance, the reducing transformation is proposed and compared with the genetic algorithm (GA). Firstly, the identification method is verified using the simulated data. Secondly, the validation is then confirmed by measured data from one machine. The corresponding computed transient and steady state currents agree well with the measured data. The results obtained show the superiority of the proposed Alienor method versus GA in terms of computing time.
The document summarizes research on thinning semi-elliptical and quarter-elliptical antenna arrays using genetic algorithm optimization to reduce side-lobe levels. Key points:
1) Genetic algorithms were used to optimize thinning of semi-elliptical and quarter-elliptical arrays with uniform excitation and spacing to produce narrow beams without degrading performance.
2) Simulation results showed thinning using genetic algorithms reduced side-lobe levels of the arrays compared to fully populated arrays.
3) Maximum side-lobe levels, beamwidths, and other metrics were tabulated for various array sizes and geometries, indicating thinning effectively lowered side-lobes.
Economic Load Dispatch Problem with Valve – Point Effect Using a Binary Bat A...IDES Editor
This paper proposes application of BAT algorithm
for solving economic load dispatch problem. BAT
algorithmic rule is predicated on the localization
characteristics of micro bats. The proposed approach has
been examined and tested with the numerical results of
economic load dispatch problems with three and five
generating units with valve - point loading without
considering prohibited operating zones and ramp rate limits.
The results of the projected BAT formula are compared with
that of other techniques such as lambda iteration, GA, PSO,
APSO, EP, ABC and basic principle. For each case, the
projected algorithmic program outperforms the answer
reported for the existing algorithms. Additionally, the
promising results show the hardness, quick convergence
and potency of the projected technique.
Daily Peak Load Forecast Using Artificial Neural NetworkIJECEIAES
The document describes a study that used an artificial neural network model to forecast daily peak electrical loads. The network was trained using historical daily peak load and temperature data from Bangalore, India. It had 24 input nodes for the load and temperature data, a single hidden layer with varying numbers of nodes, and 12 output nodes to forecast the daily peak load for each month. The optimal network structure was found to have 21 nodes in the hidden layer. When trained and tested on load data from March 2017, it produced forecasts with errors less than 2% compared to over 10% for conventional linear and polynomial fitting methods. The neural network approach was thus found to provide more accurate short-term load forecasting.
Khaled eskaf presentation predicting power consumption using genetic algorithmKhaled Eskaf
The document discusses predicting short-term electrical energy consumption using a dynamic model and genetic algorithm. It proposes extracting features from historical energy consumption time series data using a dynamic system model to determine impulse forces and damping factors. A genetic algorithm is then used to predict future consumption values based on these features, with the ability to continuously learn from new data online. The approach is evaluated using root-mean-square error and shown to achieve accurate predictions between 5x10-5 to 1x10-4 kilowatt hours.
This document summarizes research on analyzing the steady-state performance of a self-excited induction generator using three optimization techniques: genetic algorithms, pattern search, and quasi-Newton methods. It provides background on induction generators and how they can operate as self-excited generators by connecting capacitors to the stator terminals. The document presents the standard steady-state equivalent circuit model and derives nonlinear equations that are solved using the three optimization techniques to determine unknown parameters. The performance of the self-excited induction generator is then evaluated based on the determined parameters.
Dwindling of real power loss by using Improved Bees Algorithmpaperpublications3
Abstract: In this paper, a new Improved Bees Algorithm (IBA) is proposed for solving reactive power dispatch problem. The aim of this paper is to utilize an optimization algorithm called the improved Bees Algorithm, inspired from the natural foraging behaviour of honey bees, to solve the reactive power dispatch problem. The IBA algorithm executes both an exploitative neighbourhood search combined with arbitrary explorative search. The proposed Improved Imperialist Competitive Algorithm (IBA) algorithm has been tested on standard IEEE 57 bus test system and simulation results show clearly the high-quality performance of the projected algorithm in reducing the real power loss.
Keywords: Optimal Reactive Power, Transmission loss, honey bee, foraging behaviour, waggle dance, bee’s algorithm, swarm intelligence, swarm-based optimization, adaptive neighbourhood search, site abandonment, random search.
Optimization of the Thyristor Controlled Phase Shifting Transformer Using PSO...IJECEIAES
This document summarizes an article that investigates optimizing the placement and sizing of a Thyristor Controlled Phase Shifting Transformer (TCPST) and Thyristor Controlled Series Capacitor (TCSC) combination on a 30-bus power system using a Particle Swarm Optimization (PSO) algorithm. The PSO algorithm was used to determine the optimal location and ratings of the TCPST-TCSC devices to minimize power losses. Implementing the optimized TCPST-TCSC combination resulted in a 46.47% reduction in power losses, outperforming the use of capacitor banks alone which achieved a 42.03% reduction. The TCPST-TCSC solution found with PSO also performed
Enhancing radial distribution system performance by optimal placement of DST...IJECEIAES
In this paper, A novel modified optimization method was used to find the optimal location and size for placing distribution Static Compensator in the radial distribution test feeder in order to improve its performance by minimizing the total power losses of the test feeder, enhancing the voltage profile and reducing the costs. The modified grey wolf optimization algorithm is used for the first time to solve this kind of optimization problem. An objective function was developed to study the radial distribution system included total power loss of the system and costs due to power loss in system. The proposed method is applied to two different test distribution feeders (33 bus and 69 bus test systems) using different Dstatcom sizes and the acquired results were analyzed and compared to other recent optimization methods applied to the same test feeders to ensure the effectiveness of the used method and its superiority over other recent optimization mehods. The major findings from obtained results that the applied technique found the most minimized total power loss in system, the best improved voltage profile and most reduction in costs due power loss compared to other methods.
Coordinated Placement and Setting of FACTS in Electrical Network based on Kal...IJECEIAES
To aid the decision maker, the optimal placement of FACTS in the electrical network is performed through very specific criteria. In this paper, a useful approach is followed; it is based particularly on the use of KalaiSmorodinsky bargaining solution for choosing the best compromise between the different objectives commonly posed to the network manager such as the cost of production, total transmission losses (Tloss), and voltage stability index (Lindex). In the case of many possible solutions, Voltage Profile Quality is added to select the best one. This approach has offered a balanced solution and has proven its effectiveness in finding the best placement and setting of two types of FACTS namely Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) in the power system. The test case under investigation is IEEE-14 bus system which has been simulated in MATLAB Environment.
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
Power losses reduction of power transmission network using optimal location o...IJECEIAES
Due to the growth of demand for electric power, electric power loss reduction takes great attention for the power utility. In this paper, a low-level generation or distributed generation (DG) has been used for transmission power losses reduction. Karbala city transmission network (which is the case study) has been represented by using MATLAB m-file to study the load flow and the power loss for it. The paper proposed the particle swarm optimization (PSO) technique in order to find the optimal number and allocation of DG with the objective to decrease power losses as possible. The results show the effect of the optimal allocation of DG on power loss reduction.
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.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This document presents a study that uses Particle Swarm Optimization (PSO) technique to solve the Dynamic Economic Dispatch (DED) problem for a 9-bus power system with 3 generators over a 24-hour period. The objective is to determine the optimal generator outputs at each hour to minimize total generation costs while satisfying system constraints. PSO is applied to find the optimal solution by updating generator output positions based on personal and global best cost values. Results found the minimum cost schedule for each generator over the 24 hours while ensuring system limits were not violated.
Performance comparison of distributed generation installation arrangement in ...journalBEEI
Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.
The document summarizes research on using a genetic algorithm to optimize the location and parameters of Flexible AC Transmission System (FACTS) devices in a power system. It first introduces FACTS devices and their ability to control power flow. It then describes using a genetic algorithm to simultaneously determine the optimal type, location, and rating of FACTS devices with the objectives of minimizing generation costs and power losses/voltage deviations. The methodology is tested on the IEEE 30-bus system with different FACTS device types. The results indicate the genetic algorithm approach can effectively determine the configuration of FACTS devices that increase system loadability.
Experimental dataset to develop a parametric model based of DC geared motor i...IJECEIAES
This paper presents the application of a System Identification based on Particle Swarm Optimization (PSO) technique to develop parametric model of experimental dataset of DC Geared motor in feeder machine. The experimental was conducted to measure the input (voltage) and output (speed) data. The actual data is used to be optimized using PSO algorithm. The parameter emphasized is Time, Man Square Error (MSE) and Average Time. One of the best model has been chosen based on the optimum parameters.
Static VAR Compensators (SVCs) is a Flexible Alternating Current Transmission System (FACTS) device that can control the power flow in transmission lines by injecting capacitive or inductive current components at the midpoint of interconnection line or in load areas. This device is capable of minimizing the overall system losses and concurrently improves the voltage stability. A line index, namely SVSI becomes indicator for the placement of SVC and the parameters of SVCs are tuned by using the multi-objective evolutionary programming technique, effectively able to control the power. The algorithm was tested on IEEE-30 Bus Reliability Test System (RTS). Comparative studies were conducted based on the performance of SVC in terms of their location and sizing for installations in power system.
Distribution network reconfiguration for loss reduction using PSO method IJECEIAES
In recent years, the reconfiguration of the distribution network has been proclaimed as a method for realizing power savings, with virtually zero cost. The current trend is to design distribution networks with a mesh network structure, but to operate them radially. This is achieved by the establishment of an appropriate number of switchable branches which allow the realization of a radial configuration capable of supplying all of the normal defects in the box of permanent defect. The purpose of this article is to find an optimal reconfiguration using a Meta heuristic method, namely the particle swarm optimization method (PSO), to reduce active losses and voltage deviations by taking into account certain technical constraints. The validity of this method is tested on a 33-IEEE test network and the results obtained are compared with the results of basic load flow.
TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithmpaperpublications3
Abstract: Flexible Alternating Current Transmission Systems (FACTS) devices represents a technological development in electrical power systems to have a tendency to generate the power with minimum price and less time that fulfill our requirement according to our need. Now a days Flexible AC Transmission System (FACTS) devices play a vital role in boost the power of system performance and power transfer capability. TCSC is an important member of family. In practical TCSC implementation, several such basic compensators may be connected in series to obtain the desired voltage rating and operating characteristics, so its placement is very important. This paper represent a meta heuristic hybrid Algorithm of Artificial Bee Colony (ABC) and Differential Evolution (DE) for finding the best placement and parameter setting of Thyristor Controlled Series capacitor to attain optimum power flow (OPF) of grid network. The proposed technique is tested at IEEE-30 bus test System. Result shows that the selected technique is one of the best for placement of TCSC for Secured optimum Power Flow (OPF).
Keywords: Optimal placement, Severity index, stressed power system, System loadability, TCSC, Hybrid DE/ABC.
Title: TCSC Placement Problem Solving Using Hybridization of ABC and DE Algorithm
Author: Ritesh Diwan, Preeti Sahu
ISSN 2349-7815
International Journal of Recent Research in Electrical and Electronics Engineering (IJRREEE)
Paper Publications
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
Convergence Parameter Analysis for Different Metaheuristic Methods Control Co...IJPEDS-IAES
This paper is an extension of our previous work, which discussed the
difficulty in implementing different methods of resistance emulation
techniques on the hardware due to its control constant estimation delay. In
order to get rid of the delay this paper attempts to include the meta-heuristic
methods for the control constants of the controller. To achieve the minimum
Total Harmonic Disturbance (THD) in the AC side of the converter modern
meta-heuristic methods are compared with the traditional methods. The
convergence parameters, which are primary for the earlier estimation of the
control constants, are compared with the measured parameters, tabulated and
tradeoff inference is done among the methods. This kind of implementation
does not need the mathematical model of the system under study for finding
the control constants. The parameters considered for estimation are
population size, maximum number of epochs, and global best solution of the
control constants, best THD value and execution time. MatlabTM /Simulink
based simulation is optimized with the M-file based optimization techniques
like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo
Search Algorithm, Gravity Search Algorithm, Harmony Search Algorithm
and Bat Algorithm.
FACTS device installation in transmission system using whale optimization alg...journalBEEI
As the world is progressing forward, the load demand in the power system has been continuously increasing day by day. This situation has forced the power system to operate under stress condition due to its limitation. Therefore, due to the stressed condition, the transmission losses faced higher increment with a lower minimum voltage. Theoretically, the installation of the Flexible AC Transmission System (FACTS) device can solve the problem experienced by the power system. This paper presents the whale optimization algorithm for loss minimization using FACTS devices in the transmission system. Thyristor controlled series compensator (TCSC) is chosen for this study. In this study, WOA is developed to identify the optimal sizing of FACTS device for loss minimization in the power system. IEEE 30- bus RTS was used as the test system to validate the effectiveness of the proposed algorithm
Comparison of optimization technique of power system stabilizer by using geaIAEME Publication
This document compares two optimization techniques - phase compensation and genetic algorithm (GA) - for tuning the parameters of a power system stabilizer (PSS). The PSS helps damp low-frequency oscillations in power systems. The phase compensation technique involves designing phase lead compensators to provide the required phase shift. The GA approach uses genetic operators like selection, crossover and mutation to evolve optimal PSS parameters over generations. Both techniques are applied to a single machine connected to an infinite bus system to optimize the PSS gain, washout time constant and lead/lag time constants. The effectiveness of each technique in enhancing power system stability is then evaluated.
T04201162168Optimal Allocation of FACTS Device with Multiple Objectives Using...IJMER
In this paper Multi objective functions are simultaneously considered as the indexes of the system performance minimize total generation fuel cost and maximize system load-ability within system security margin. To find the optimal location and optimal value for Thyristor Controlled Series Compensator (TCSC) using optimization technique Genetic Algorithm (GA) to maximize system load-ability and minimize the system losses considering multi objectives optimization approach. A GA based Optimal Power Flow (OPF) is proposed to determine the type of FACTS (Flexible AC Transmission system) controllers, its optimal location and rating of the devices in power systems. The value of TCSC and line losses is applied as measure of power system performance. The type of FACTS controllers are used and modeled for steady-state studies: TCSC, minimize total generation fuel cost and maximize system load-ability within system security margin. Simulations will be carrying on IEEE30 bus power system for type of FACTS devices.
Modeling and Simulation of power system using SMIB with GA based TCSC controllerIOSR Journals
This document summarizes a study that uses genetic algorithms to tune a thyristor-controlled series compensator (TCSC) controller to improve the stability of a single-machine infinite-bus (SMIB) power system model. The study models the SMIB system and implements a TCSC to damp oscillations. Genetic algorithms are used to optimize the TCSC controller parameters. Simulation results show that the genetically-tuned TCSC controller more effectively damps oscillations compared to the system without a TCSC controller.
A genetic algorithm for the optimal design of a multistage amplifier IJECEIAES
The optimal sizing of analog circuits is one of the most complicated processes, because of the number of variables taken into, to the number of required objectives to be optimized and to the constraint functions restrictions. The aim is to automate this activity in order to accelerate the circuits design and sizing. In this paper, we deal with the optimization of the three stage bipolar transistor amplifier performances namely the voltage gain (AV), the input impedance (ZIN), the output impedance (ZOUT), the power consumption (P) and the low and the high cutoff frequency (FL,FH), through the Genetic Algorithm (GA). The presented optimization problem is of multi-dimensional parameters, and the trade-off of all parameters. In fact, the passive components (Resistors and Capacitors) are selected from manufactured constant values (E12, E24, E48, E96, E192) for the purpose of reduce the cost of design; also, the intrinsic parameters of transistors (hybrid parameters and the junction capacitances) are considered variables in order not to be limited in design. SPICE simulation is used to validate the obtained result/performances.
Similar to Optimized placement of multiple FACTS devices using PSO and CSA algorithms (20)
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%.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
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.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
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.
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%.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
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. Int J Elec & Comp Eng ISSN: 2088-8708
Optimized placement of multiple FACTS devices using PSO and CSA algorithms (Basanagouda Pati)
3351
in [27, 28] are used here for multi-facts device placement. This paper is done for minimizing the total cost of
the generation and FACTS devices (like SVC, TCSC & UPFC). The optimal location and size are identified.
Section 2 consists Problem Formulation for optimal location of multiple FACTS are described. Section 3
consist of Problem solution methods; Section 4 consists of simulation results. Finally, a conclusion about
the results of simulation is deduced in Section 5.
2. PROBLEM FORMULATION
The generation cost and the cost of FACTS devices are the major economic sources. Here in
the optimal power flow the cost of generation minimization and the FACTs device placement with minimum
possible or optimal cost has to be identified. Bidding cost is considered as the thermal system cost curve so
the bidding cost can be represented as [25],
𝐹𝑖(𝑃𝑔𝑖) = 𝑎𝑖 + 𝑏𝑖 𝑃𝑔𝑖 + 𝑐𝑖 𝑃𝑔𝑖
2
(1)
the incremental cost can be represented as below,
𝐼𝐶𝑖(𝑃𝑔𝑖) = 𝑏𝑖 + 2𝑐𝑖 𝑃𝑔𝑖 (2)
deregulated power system optimal power flow equation is given below,
𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒: ∑ 𝐹𝑖(𝑃𝑔𝑖)𝑛
𝑖=1 (3)
𝑠𝑢𝑏𝑗𝑒𝑐𝑡𝑒𝑑𝑡𝑜: ∑ 𝑃𝑔𝑖 = 𝑃𝑑
𝑁 𝑔
𝑃 𝑔𝑖
(4)
𝑃𝑖𝑚𝑖𝑛 < 𝑃𝑔𝑖 < 𝑃𝑖𝑚𝑎𝑥, 𝑖 𝜖[1, 𝑁𝑔] (5)
when ∑ 𝑃𝑖𝑚𝑖𝑛
𝑁 𝑔
𝑖=1
> 𝑃𝑑 𝑜𝑟 ∑ 𝑃𝑖𝑚𝑎𝑥
𝑁 𝑔
𝑖=1
= 𝑃𝑑, -no feasible solution,
when ∑ 𝑃𝑖𝑚𝑖𝑛
𝑁 𝑔
𝑖=1
= 𝑃𝑑, -each seller is contracted amount is at its capacity lower limit,
when ∑ 𝑃𝑖𝑚𝑖𝑛
𝑁 𝑔
𝑖=1
< 𝑃𝑑 and ∑ 𝑃𝑖𝑚𝑖𝑛
𝑁 𝑔
𝑖=1
> 𝑃𝑑-non-trivial case.
Here,
𝐹𝑖(𝑃𝑔𝑖) − 𝑐𝑜𝑠𝑡𝑜𝑓𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑜𝑟𝑖
𝑃𝑔𝑖 − 𝑃𝑜𝑤𝑒𝑟𝑖𝑛𝑀𝑊𝑜𝑓𝑖 𝑡ℎ
𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑜𝑟
𝑎𝑖, 𝑏𝑖, 𝑐𝑖 − 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡𝑐𝑜 − 𝑜𝑟𝑑𝑖𝑛𝑎𝑡𝑒
𝑃𝑖𝑚𝑖𝑛, 𝑃𝑖𝑚𝑎𝑥 − 𝑚𝑖𝑛𝑖𝑚𝑢𝑚𝑎𝑛𝑑𝑚𝑎𝑥𝑖𝑚𝑢𝑚𝑙𝑖𝑚𝑖𝑡𝑠𝑜𝑓𝑖 𝑡ℎ
𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑜𝑟
𝑃𝑑 − 𝑃𝑜𝑤𝑒𝑟𝑑𝑒𝑚𝑎𝑛𝑑𝑖𝑛𝑀𝑊
𝑛, 𝑁𝑔 − 𝑁𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑜𝑟𝑠
facts devices costs;
𝐶 𝑇𝐶𝑆𝐶 = 0.0015𝑆 𝑇𝐶𝑆𝐶
2
− 0.713𝑆 𝑇𝐶𝑆𝐶 + 153.75 (6)
𝐶𝑆𝑉𝐶 = 0.0003𝑆𝑆𝑉𝐶
2
− 0.3051𝑆𝑆𝑉𝐶 + 127.38 (7)
𝐶 𝑈𝑃𝐹𝐶 = 0.0003𝑆 𝑈𝑃𝐹𝐶
2
− 0.2691𝑆 𝑈𝑃𝐹𝐶 + 188.2 (8)
here;
𝐼𝐶 𝑑𝑒𝑣𝑖𝑐𝑒𝑠 − 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑐𝑜𝑠𝑡𝑜𝑓𝐹𝐴𝐶𝑇𝑆𝑑𝑒𝑣𝑖𝑐𝑒𝑠𝑖𝑛 $
𝐶 𝑇𝐶𝑆𝐶 − 𝑇𝐶𝑆𝐶𝑐𝑜𝑠𝑡𝑝𝑒𝑟𝐾𝑉𝐴𝑅𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 in $
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 3350 - 3357
3352
𝐶𝑆𝑉𝐶 − 𝑆𝑉𝐶𝑐𝑜𝑠𝑡𝑝𝑒𝑟𝐾𝑉𝐴𝑅𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑𝑖𝑛 $
𝐶 𝑈𝑃𝐹𝐶 − 𝑈𝑃𝐹𝐶𝑐𝑜𝑠𝑡𝑝𝑒𝑟𝐾𝑉𝐴𝑅𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑𝑖𝑛 $
𝑆 𝑇𝐶𝑆𝐶 − 𝑇𝐶𝑆𝐶𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑖𝑛𝑀𝑉𝐴𝑅
𝑆𝑆𝑉𝐶 − 𝑆𝑉𝐶𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑖𝑛𝑀𝑉𝐴𝑅
𝑆 𝑈𝑃𝐹𝐶 − 𝑈𝑃𝐹𝐶𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑖𝑛𝑀𝑉𝐴𝑅
Considering the above constraints entire cost function can be represented as below [6].
𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒𝑇𝑜𝑡𝑎𝑙𝐶𝑜𝑠𝑡 = ∑ 𝐹𝑖(𝑃𝑔𝑖)𝑛
𝑖=1 + 𝐼𝐶 𝑑𝑒𝑣𝑖𝑐𝑒 (9)
3. SOLUTION METHODS
For the problem shown in (9) is the objective function to solve that many techniques can be used.
Here PSO algorithm which is the faster algorithm and the CSA algorithm which gives guaranteed results are
considered for the solution. The algorithm explanation is given below.
3.1. Particle swarm optimization (PSO)
The algorithm is formed with the behavior of insects/fish on its behavior of food searching. Steps of
algorithm described given below.
- The Nsize of the swarm, X-control variable (generated power Pg) are initialized.
- Initial population of Pg is given as within the power limit. And initial velocity of the swarm particles (Vj)
is taken as zero.
- For each population calculate fuel cost (F) and find velocitieswith given formula (10).and increment
the iteration.
- Eachparticle is personal best (Pbest) of its own Pgvalue. Then the X value which is responsible for
the lower cost value is taken as global best (Gbest). Then velocity function is calculated using
the following equation,
𝑉𝑗(𝑖) = 𝑉𝑗(𝑖 − 1) + 𝑐1 𝑟1[𝑃𝑏𝑒𝑠𝑡𝑗 − 𝑋𝑗(𝑖 − 1)] + 𝑐2 𝑟2[𝐺 𝑏𝑒𝑠𝑡 − 𝑋𝑗(𝑖 − 1)] (10)
where 𝑗 = 1,2, … , 𝑁
here,
𝑐1, 𝑐2 𝑎𝑟𝑒𝑐𝑜𝑔𝑛𝑖𝑡𝑖𝑣𝑒𝑎𝑛𝑑𝑠𝑜𝑐𝑖𝑎𝑙𝑙𝑒𝑎𝑟𝑛𝑖𝑛𝑔𝑟𝑎𝑡𝑒𝑠𝑡𝑎𝑘𝑒𝑛 2
𝑟1, 𝑟2 𝑎𝑟𝑒𝑢𝑛𝑖𝑓𝑜𝑟𝑚𝑦𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑑𝑟𝑎𝑛𝑑𝑜𝑚𝑠𝑖𝑛𝑟𝑎𝑛𝑔𝑒 0 𝑎𝑛𝑑 1
- Then the X value is updated with the following equation
𝑋𝑗(𝑖) = 𝑋𝑗(𝑖 − 1) + 𝑉𝑗(𝑖) (11)
- Then go to step (c), do it till the stop criteria.
3.2. Cuckoo search algorithm (CSA)
The Cuckoo search algorithm is based on the cuckoo bird on behavior of its breeding. The cuckoo
bird can’t build the nest. It depends on the host bird nest for laying eggs and hatching it. But host bird nest
not allows to do so. It may abandon the nest or pushes the birds’ eggs down. But cuckoo lays eggs similar to
the host bird and if it hatches the cuckoo chicks mimics the sound of the host bird. So, finding the best nest to
make survive the cuckoo birds makes a fine search that is represented as the mathematical equation steps are
following.
- The initial population of X variable in n host nests is randomly generated.
- A cuckoo is selected by levy random distribution and evaluated the objective function for all the host
nests.
- Randomly selected nest iscompared with the objective which is randomly selected and calculated.
If the new cuckoo fits then replace the old cuckoo.
- Remaining nests are abandoned with the fraction of Pa and best ones are saved.
4. Int J Elec & Comp Eng ISSN: 2088-8708
Optimized placement of multiple FACTS devices using PSO and CSA algorithms (Basanagouda Pati)
3353
- Rank the solution; find the best cuckoo.
- Increase the iteration and go to step second step.
- Do it till termination
The proposed solution algorithm is described:
Step 1: Initialize line and bus data of the power system, contingency data, all constraints, and PSO/CSA
parameters.
Step 2: Initialize population of particles with random numbers and velocities/new nest representing FACTS
devices location & size.
Step 3: Set iteration index iteration = 0.
Step 4: The particle carries the location and size of FACTS devices updates the line-data at the reactance
column and in bus-data power injection column. Determine the load level and output power. Conduct OPF
incorporating FACTS devices, for normal and contingency states. Compute the operating cost and required
devices capacities for each state.
Step 5: Calculate cost with FACTS using operating costs of all states and their associated probabilities to
occur. Calculate devices investment cost using (8).
Step 6: Evaluate the value of the objective function (9) subject to all the constraints (4 & 5). If any of
the constraint violation penalty is added in cost. The calculated value of the fitness function is served as
a fitness value of a particle/cuckoo.
Step 7: Each particle objective is calculated with the personal best, local best. If the fitness value is lower
than local best, set this value as the current local best, and save the particle position corresponding to this
local best value.
Step 8: Select the minimum value of local best from all particles to be the current global best, Global best,
and record the particle position corresponding to this Global best value.
Step 9: Update each particle velocity and also position.
Step 10: If the maximum number of iterations is reached, the particle/cuckoo associated with the current
Global best is the optimal solution. Otherwise, set iteration = iteration + 1 and goto Step 4. And repeat till
termination
4. RESULTS AND DISCUSSION
Test system is 3-seller system and two solution algorithms are used.Here the no FACTs devices
results are the conventional methods. ThePSO and CSA are taken here. As shown in the results the fitness
value of PSO and CSA in [28], it varies from $8340 to 8190. As it is economic load dispatch the loss
consideration also based on the loss matrix. When the same 3-seller system is used in the optimal power flow
the cost of the generation reduces to $ 8034.4. we use the same 3-seller system as the test system and we
implement the facts devices with inclusion of investment cost.
The FACTS devices considered here are SVC, TCSC and UPFC. SVC and UPFC models are taken
as reactive power model and the TCSC is taken as reactance model. The objective function discussed in (1) is
taken as fitness equation with voltage limit and power flow constraints. The well-known metaheuristic
algorithm called PSO and CSA algorithms are used for testing the fitness function for without facts devices.
Then the (9) is used for testing with FACTS devices. ICdevices variable can be replaced with each facts
device cost equation respectively. The results obtained are discuss below.
4.1. PSO algorithm
PSO algorithm as explained in the solution technology section the MATLAB code is implemeted to
solve both (1) and (2). The Figure 1 shows the convergence graph of the PSO algorithm for without and with
placement of SVC, TCSC and UPFC. From that it can be seen that the UPFC gives reduced cost including
the cost of UPFC. Figure 2 shows the voltage profile of NO facts device condition, SVC placed, TCSC
placed and UPFC placed. The performance of votlage profile is better and TCSC is not performing well,
as the cost increases. Figure 3 shows the power generated at generator number 1, 2, 3, 6 and 8. It can be seen
from Figure 3 that G3, G6 and G8 has significant reduction in generated total power when the FACTS
devices are placed. Table 1 shows the generated power in IEEE-14 bus system. Table 2 shows the location,
size, cost and loss of the 3-seller system with PSO algorithm. It can be seen from [28] the cost from $ 8100
(approx.) to $ 7910.4 when using UPFC including the investment cost of UPFC.
4.2. CSA algorithm
Figures 4-6 shows the results taken from CSA for FACTS device placement and Tables 3 and 4
shows the numerical results. Using CSA cost is still reduced to $ 7907.5 with UPFC.
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Figure 1. Convergence graph of PSO algorithm with and without SVC, TCSC and UPFC
Figure 2. Voltage profile with and without SVC,
TCSC and UPFC
Figure 3. Generated power with and without SVC,
TCSC and UPFC
Table 1. Generated power in MW
Gen. nos Generated power in MW
No FACTS SVC TCSC UPFC
G1 186.75149 192.454 191.048 191.687
G2 35.820405 36.9311 36.112 37.0097
G3 44.052839 23.9131 20.7523 19.8806
G6 0 8.20814 9.92287 12.39808
G8 0 0 6.29444 0
Table 2. Location, size, cost and loss of the 3- seller system with PSO algorithm
Location Size Total Cost in $ Loss in MW
NO FACTS - - 8054.4 7.6247
SVC 4 84.27 MVAR 7931.9 2.5061
TCSC 6 to 11 0.75 ohms 8977 5.1297
UPFC 13 27.953 MVAR 7910.4 1.9754
6. Int J Elec & Comp Eng ISSN: 2088-8708
Optimized placement of multiple FACTS devices using PSO and CSA algorithms (Basanagouda Pati)
3355
Figure 4. Convergence graph of CSA algorithm with and without SVC, TCSC and UPFC
Figure 5. Voltage profile with and without SVC,
TCSC and UPFC
Figure 6. Generated power with and without SVC,
TCSC and UPFC
Table 3. Generated power in MW
Gen. nos Generated power in MW
No FACTS SVC TCSC UPFC
G1 186.8083133 187.7138 188.8311 208.7254
G2 35.97583531 36.09213 34.69885 35.30854
G3 42.57066531 20.33008 13.26914 1.799593
G6 0 16.49721 16.97138 16.22596
G8 1.315076167 0 11.00931 0
Table 4. Location, size, cost and loss of the 3- seller system with CSA algorithm
Location Size Total Cost in $ Loss in MW
NO FACTS - - 8054.4 7.6699
SVC 13 26.7432 MVAR 7914.5 1.6333
TCSC 6 to 11 1 pu 8114.8 5.7798
UPFC 13 28.2819 MVAR 7907.5 3.0595
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5. CONCLUSION
The MATLAB implementation of the placement of multiple FACTS devices on the IEEE 14 bus
system and the results were inferred. The optimization algorithm that was used for the placement of
the multiple FACTS devices included PSO and CSA algorithm. The results obtained from the CSA
implementation outperformed PSO algorithm and the cost function reduced value while optimizing using
the CSA algorithm. So, compared to before placement and after placement of multi-facts devices the total
cost of generation reduces even including the FACTS device cost.
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BIOGRAPHIES OF AUTHORS
Mr. Basanagouda Patil Received the M. Tech in PES from BEC Bagalkot Karnatakain year
2010.At Present He is Pursuing Ph.D (Power System) fromSDMCET Dharwad & Life Member of
Indian Society for Technical Education (ISTE), His Research Interest in Power system & Facts
Devices
Dr. S. B. Karajgi Received the M.E in REC Warangal 1987, & Ph. D from NITK Surathkal in
2014. Presently He is Working asva Professor in Department of EEE SDMCET Dharwad
Karnataka. HIS Research Area interests in Power System Operation & Distribution Generation,
Life Member of Indian Society Technical Education (ISTE).