Optimal power flow solutions for power system operations using moth-flame optimization algorithm

Optimal power flow (OPF) has gained a growing attention from electrical power researchers since it is a significant tool that assists utilities of power system to determine the optimal economic and secure operation of the electric grid. The key OPF objective is to optimize a certain objective functi...

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Main Author: Salman Ameen Ali, Abdullah Alabd
Format: Thesis
Language:English
Published: 2021
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Online Access:http://umpir.ump.edu.my/id/eprint/34392/1/Optimal%20power%20flow%20solutions%20for%20power%20system.pdf
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spelling my-ump-ir.343922022-06-15T04:02:47Z Optimal power flow solutions for power system operations using moth-flame optimization algorithm 2021-10 Salman Ameen Ali, Abdullah Alabd TK Electrical engineering. Electronics Nuclear engineering Optimal power flow (OPF) has gained a growing attention from electrical power researchers since it is a significant tool that assists utilities of power system to determine the optimal economic and secure operation of the electric grid. The key OPF objective is to optimize a certain objective function such as: minimization of total fuel cost, emission, real power transmission loss, voltage deviation, etc. while fulfilling certain operation constraints like bus voltage, line capacity, generator capability and power flow balance. Optimal reactive power dispatch (ORPD) is a sub-problem of optimal power flow. ORPD has a considerable impact on the economic and the security of the electric power system operation and control. ORPD is considered a mixed nonlinear problem because it contains continuous and discrete control variables. Another sub-problem of OPF is Economic dispatch (ED) which one of the complex problems in the power system which its purposes is to determine the optimal allocation output of generator unit to satisfy the load demand at the minimum economic cost of generation while meeting the equality and inequality constraints. In this thesis, a recent metaheuristic nature-inspired optimization algorithm namely: Moth-Flame Optimizer (MFO) is applied to solve the two subproblems of Optimal power flow (OPF) namely: Economic dispatch (ED) and Optimal reactive power dispatch (ORPD) simultaneously. Three objective functions will be considered: generation cost minimization, transmission power loss minimization, and voltage deviation minimization using a weighted factor. The IEEE 30-bus test system and IEEE 57-bus test system will be employed, to investigate the effectiveness of the proposed MFO in solving the above-mentioned problems. Then the obtained MFO methods results is compared with other reported well-known methods. The comparison proves that MFO offers a better result compared to the other selected methods. In IEEE 30-bus test system, MFO outperform the other optimization methods with 967.589961$/h compared to 971.411400 $/h, 983.738069$/h, 975.346233$/h, 985.198050$/h, 1035.537820$/h for Improved Grey Wolf Optimizer (IGWO), Grey Wolf Optimizer (GWO), Ant Loin Optimizer (ALO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) respectively. In IEEE 57-bus test system, MFO offers a minimization of 19.16% compared to 19.03% and 18.98% for Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) respectively. Moreover, the MFO have speedy convergence rate and smooth curves more than the other algorithms. 2021-10 Thesis http://umpir.ump.edu.my/id/eprint/34392/ http://umpir.ump.edu.my/id/eprint/34392/1/Optimal%20power%20flow%20solutions%20for%20power%20system.pdf pdf en public masters Universiti Malaysia Pahang Faculty of Electrical & Electronics Engineering
institution Universiti Malaysia Pahang Al-Sultan Abdullah
collection UMPSA Institutional Repository
language English
topic TK Electrical engineering
Electronics Nuclear engineering
spellingShingle TK Electrical engineering
Electronics Nuclear engineering
Salman Ameen Ali, Abdullah Alabd
Optimal power flow solutions for power system operations using moth-flame optimization algorithm
description Optimal power flow (OPF) has gained a growing attention from electrical power researchers since it is a significant tool that assists utilities of power system to determine the optimal economic and secure operation of the electric grid. The key OPF objective is to optimize a certain objective function such as: minimization of total fuel cost, emission, real power transmission loss, voltage deviation, etc. while fulfilling certain operation constraints like bus voltage, line capacity, generator capability and power flow balance. Optimal reactive power dispatch (ORPD) is a sub-problem of optimal power flow. ORPD has a considerable impact on the economic and the security of the electric power system operation and control. ORPD is considered a mixed nonlinear problem because it contains continuous and discrete control variables. Another sub-problem of OPF is Economic dispatch (ED) which one of the complex problems in the power system which its purposes is to determine the optimal allocation output of generator unit to satisfy the load demand at the minimum economic cost of generation while meeting the equality and inequality constraints. In this thesis, a recent metaheuristic nature-inspired optimization algorithm namely: Moth-Flame Optimizer (MFO) is applied to solve the two subproblems of Optimal power flow (OPF) namely: Economic dispatch (ED) and Optimal reactive power dispatch (ORPD) simultaneously. Three objective functions will be considered: generation cost minimization, transmission power loss minimization, and voltage deviation minimization using a weighted factor. The IEEE 30-bus test system and IEEE 57-bus test system will be employed, to investigate the effectiveness of the proposed MFO in solving the above-mentioned problems. Then the obtained MFO methods results is compared with other reported well-known methods. The comparison proves that MFO offers a better result compared to the other selected methods. In IEEE 30-bus test system, MFO outperform the other optimization methods with 967.589961$/h compared to 971.411400 $/h, 983.738069$/h, 975.346233$/h, 985.198050$/h, 1035.537820$/h for Improved Grey Wolf Optimizer (IGWO), Grey Wolf Optimizer (GWO), Ant Loin Optimizer (ALO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) respectively. In IEEE 57-bus test system, MFO offers a minimization of 19.16% compared to 19.03% and 18.98% for Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA) respectively. Moreover, the MFO have speedy convergence rate and smooth curves more than the other algorithms.
format Thesis
qualification_level Master's degree
author Salman Ameen Ali, Abdullah Alabd
author_facet Salman Ameen Ali, Abdullah Alabd
author_sort Salman Ameen Ali, Abdullah Alabd
title Optimal power flow solutions for power system operations using moth-flame optimization algorithm
title_short Optimal power flow solutions for power system operations using moth-flame optimization algorithm
title_full Optimal power flow solutions for power system operations using moth-flame optimization algorithm
title_fullStr Optimal power flow solutions for power system operations using moth-flame optimization algorithm
title_full_unstemmed Optimal power flow solutions for power system operations using moth-flame optimization algorithm
title_sort optimal power flow solutions for power system operations using moth-flame optimization algorithm
granting_institution Universiti Malaysia Pahang
granting_department Faculty of Electrical & Electronics Engineering
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/34392/1/Optimal%20power%20flow%20solutions%20for%20power%20system.pdf
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