Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization

Power systems are complicated to be solved due to vast geographical location and are influenced by many unexpected weather events. The rapidly increasing population growth and the expansion of urban development are undoubtedly the main reasons for increasing electrical power demands that may affect...

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Main Author:	El Tawil, Naji Ammar Mansour
Format:	Thesis
Language:	English English
Published:	2021
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/26009/1/Optimal%20allocation%20and%20sizing%20of%20capacitor%20bank%20and%20distributed%20generation%20using%20particle%20swarm%20optimization.pdf http://eprints.utem.edu.my/id/eprint/26009/2/Optimal%20allocation%20and%20sizing%20of%20capacitor%20bank%20and%20distributed%20generation%20using%20particle%20swarm%20optimization.pdf
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spelling	my-utem-ep.260092023-07-07T11:26:14Z Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization 2021 El Tawil, Naji Ammar Mansour T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Power systems are complicated to be solved due to vast geographical location and are influenced by many unexpected weather events. The rapidly increasing population growth and the expansion of urban development are undoubtedly the main reasons for increasing electrical power demands that may affect the system voltage stability and the energy loss. Accurate long-term load forecasting (LTLF) is essential for load demand requirements. It is particularly significant under the influence of various weather factors, such as relative humidity and temperature. The research work presented in this thesis had investigated the effect of two additional weather parameters, namely wind speed and rainfall, in addition to the temperature and relative humidity using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in predicting the values of load demands. Moreover, the optimal allocation and sizing of the capacitor bank (C) and distributed generation (DG) were studied with the particle swarm optimization (PSO) technique to maintain the profile of bus voltages while reducing the energy loss of the network. This technique was also applied to the load incremental of 5% annually up to 40% for system planning purposes. As for the LTLF, ANFIS produced better results than ANN; and with two additional parameters of wind speed and rainfall, it delivered a more accurate prediction. The PSO algorithm allocates and determines the size of the capacitor and distributed generation in the power system. The capacitors and distributed generation are compensators that helped the power system network improve the voltage profile and reduce power loss. The proposed PSO algorithm was used with the OpenDSS engine to solve the power flow through the MATLAB and has been successful implemented in finding an optimal allocation and suitable size of the capacitor and distributed generation. In order to validate the functionality of the proposed PSO algorithm, the IEEE 14–bus and 30–bus systems were used as test systems. The research evidently indicated the PSO algorithm can be applied to the power system planning analysis for the placement and sizing of the capacitor and distributed generation while maintaining the acceptable voltage profile and minimizing the power loss. 2021 Thesis http://eprints.utem.edu.my/id/eprint/26009/ http://eprints.utem.edu.my/id/eprint/26009/1/Optimal%20allocation%20and%20sizing%20of%20capacitor%20bank%20and%20distributed%20generation%20using%20particle%20swarm%20optimization.pdf text en public http://eprints.utem.edu.my/id/eprint/26009/2/Optimal%20allocation%20and%20sizing%20of%20capacitor%20bank%20and%20distributed%20generation%20using%20particle%20swarm%20optimization.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=121272 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Electronic and Computer Engineering Sulaiman, Marizan
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Sulaiman, Marizan
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) El Tawil, Naji Ammar Mansour Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization
description	Power systems are complicated to be solved due to vast geographical location and are influenced by many unexpected weather events. The rapidly increasing population growth and the expansion of urban development are undoubtedly the main reasons for increasing electrical power demands that may affect the system voltage stability and the energy loss. Accurate long-term load forecasting (LTLF) is essential for load demand requirements. It is particularly significant under the influence of various weather factors, such as relative humidity and temperature. The research work presented in this thesis had investigated the effect of two additional weather parameters, namely wind speed and rainfall, in addition to the temperature and relative humidity using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in predicting the values of load demands. Moreover, the optimal allocation and sizing of the capacitor bank (C) and distributed generation (DG) were studied with the particle swarm optimization (PSO) technique to maintain the profile of bus voltages while reducing the energy loss of the network. This technique was also applied to the load incremental of 5% annually up to 40% for system planning purposes. As for the LTLF, ANFIS produced better results than ANN; and with two additional parameters of wind speed and rainfall, it delivered a more accurate prediction. The PSO algorithm allocates and determines the size of the capacitor and distributed generation in the power system. The capacitors and distributed generation are compensators that helped the power system network improve the voltage profile and reduce power loss. The proposed PSO algorithm was used with the OpenDSS engine to solve the power flow through the MATLAB and has been successful implemented in finding an optimal allocation and suitable size of the capacitor and distributed generation. In order to validate the functionality of the proposed PSO algorithm, the IEEE 14–bus and 30–bus systems were used as test systems. The research evidently indicated the PSO algorithm can be applied to the power system planning analysis for the placement and sizing of the capacitor and distributed generation while maintaining the acceptable voltage profile and minimizing the power loss.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	El Tawil, Naji Ammar Mansour
author_facet	El Tawil, Naji Ammar Mansour
author_sort	El Tawil, Naji Ammar Mansour
title	Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization
title_short	Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization
title_full	Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization
title_fullStr	Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization
title_full_unstemmed	Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization
title_sort	optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electronic and Computer Engineering
publishDate	2021
url	http://eprints.utem.edu.my/id/eprint/26009/1/Optimal%20allocation%20and%20sizing%20of%20capacitor%20bank%20and%20distributed%20generation%20using%20particle%20swarm%20optimization.pdf http://eprints.utem.edu.my/id/eprint/26009/2/Optimal%20allocation%20and%20sizing%20of%20capacitor%20bank%20and%20distributed%20generation%20using%20particle%20swarm%20optimization.pdf
_version_	1776103128360288256

Optimal allocation and sizing of capacitor bank and distributed generation using particle swarm optimization

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