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Solving the capacitor placement problem in radial distribution networks
Faculty
Engineering
Year:
2022
Type of Publication:
ZU Hosted
Pages:
Authors:
Mohammed Abdelfattah Mohamed Farahat
Staff Zu Site
Abstract In Staff Site
Journal:
Results in Engineering EL SEVIER
Volume:
Keywords :
Solving , capacitor placement problem , radial distribution
Abstract:
The optimal capacitor allocation problem is suggested in this article. This study purposes to maximize the voltage stability, minimize the power losses, and consolidate the annual net savings. Calculating the optimal locations and sizes of fixed and switched capacitors is done in two steps. The first step is using the fuzzy expert rules in calculating the most candidate buses for capacitor allocation. While the second step is using a nondominated sorting genetic algorithm II (NSGA-II) in determining the list of Pareto optimal solutions and then applying a fuzzy decision maker to pick the most compromise. To emphasize the effectiveness of the proposed method, radial distribution systems are proposed; IEEE 33-bus system and the actual Portuguese IEEE 94-bus system. To demonstrate the strength and applicability of this method, a multiobjective water cycle algorithm (MOWCA), multiobjective grey wolf optimizer (MOGWO), and other optimizers used in the published papers are used. From simulation and analysis, the proposed NSGA-II outperforms other optimizers considered for comparison in achieving the maximum percentages of minimization in a real power loss of 32.369% and 31.1011% for the 33- bus system and 25.6296% and 25.3027% for the 94-bus system, the maximum percentages of minimization in a reactive power loss of 31.7916% and 30.4948% for 33-bus system and 25.9457% and 25.8001% for the 94-bus system, the maximum annual net savings of 23,612 $ and 23,131 $ for the 33-bus system for fixed and switched capacitors, respectively, and boosting the total voltage stability, which show its superior ability to give high-grade solutions.
Author Related Publications
Mohammed Abdelfattah Mohamed Farahat, "A New Approach for Short-Term Load Forecasting Using Curve Fitting Prediction Optimized by Genetic Algorithms", IEEE, 2010
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Mohammed Abdelfattah Mohamed Farahat, "Short-Term Load Forecasting Using Curve Fitting Prediction Optimized by Genetic Algorithms", SAP journals, 2012
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Mohammed Abdelfattah Mohamed Farahat, "The Using of Curve Fitting Prediction Optimized by Genetic Algorithms for Short-Term Load Forecasting", Praise Worthy Prize, 2012
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Mohammed Abdelfattah Mohamed Farahat, "A New Artificial Neural Network Approach with Selected Inputs for Short Term Electric Load Forecasting", Praise Worthy Prize, 2008
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Mohammed Abdelfattah Mohamed Farahat, "Factors Affecting the Life Time of the Electric Joints", Cairo University, 2010
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