Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques

The fundamental requirement of hydropower system scheduling is to determine the optimal amount of generated powers for the hydro unit of the system in the scheduling horizon of 1 year or few years while satisfying the constraints of the hydroelectric system. Annual hydro generation scheduling (AHGS)...

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Main Author:	Hammid, Ali Thaeer
Format:	Thesis
Language:	English
Published:	2018
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/23423/1/Optimization%20and%20control%20of%20hydro%20generation%20scheduling%20using%20hybrid%20firefly%20algorithm%20and%20particle%20swarm%20optimization%20techniques.pdf
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spelling	my-ump-ir.234232021-11-10T02:34:12Z Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques 2018-04 Hammid, Ali Thaeer TK Electrical engineering. Electronics Nuclear engineering The fundamental requirement of hydropower system scheduling is to determine the optimal amount of generated powers for the hydro unit of the system in the scheduling horizon of 1 year or few years while satisfying the constraints of the hydroelectric system. Annual hydro generation scheduling (AHGS) is a complicated non-linear, non-convex and non-smooth optimization problem with discontinuous solution space. The model considers daily water inflows, limits on reservoir level, power generation depends on the available head of hydro units caused by power variations, start-up, and shut-down of hydro units. Moreover, hydro generation prediction typically has composite structures such as non-linearity, non-stationarity, and fluctuation due to unexpected variable of input parameters, which converts its prediction to be very tough. Artificial intelligence (AI) methods are normally selected to deal with this problem. However, they are suffering from partial optimization, falling in solutions of local minima, and low speed of convergence. To deal with these problems, this thesis introduces three approved intelligent controllers for hydropower generation. Firstly, a hybrid algorithm namely firefly particle swarm optimization (FPSO) and series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed. In the FPSO method, the local search is performed through the modified light intensity attraction step with PSO operator. Secondly, this approach hybridizing the FA with the rough algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. After that, the stationary simulation prediction model is obtained. Thirdly, a novel normalized firefly fuzzy control method (NFANN) is designed for stability control of a hydro-turbine system. Moreover, the more relaxed and simplified sufficient stability conditions are given as a new set of right-angle triangle membership function (RFANN), which has been guaranteed by strict mathematical derivation. The proposed methods tested on raw data of hydropower plant of Himreen Lake Dam. The optimal hydropower generation that observed is increased to the maximum over the actual value by PSO, SD-PSO, SD-FA, and FPSO increased by 1.5%, 2.3%, 3.1%, 2.5% respectively. The proposed SD-FA controller showed better and robustness compared to the other algorithm, which could resist the random disturbances. 2018-04 Thesis http://umpir.ump.edu.my/id/eprint/23423/ http://umpir.ump.edu.my/id/eprint/23423/1/Optimization%20and%20control%20of%20hydro%20generation%20scheduling%20using%20hybrid%20firefly%20algorithm%20and%20particle%20swarm%20optimization%20techniques.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Electrical & Electronic 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 Hammid, Ali Thaeer Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
description	The fundamental requirement of hydropower system scheduling is to determine the optimal amount of generated powers for the hydro unit of the system in the scheduling horizon of 1 year or few years while satisfying the constraints of the hydroelectric system. Annual hydro generation scheduling (AHGS) is a complicated non-linear, non-convex and non-smooth optimization problem with discontinuous solution space. The model considers daily water inflows, limits on reservoir level, power generation depends on the available head of hydro units caused by power variations, start-up, and shut-down of hydro units. Moreover, hydro generation prediction typically has composite structures such as non-linearity, non-stationarity, and fluctuation due to unexpected variable of input parameters, which converts its prediction to be very tough. Artificial intelligence (AI) methods are normally selected to deal with this problem. However, they are suffering from partial optimization, falling in solutions of local minima, and low speed of convergence. To deal with these problems, this thesis introduces three approved intelligent controllers for hydropower generation. Firstly, a hybrid algorithm namely firefly particle swarm optimization (FPSO) and series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed. In the FPSO method, the local search is performed through the modified light intensity attraction step with PSO operator. Secondly, this approach hybridizing the FA with the rough algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. After that, the stationary simulation prediction model is obtained. Thirdly, a novel normalized firefly fuzzy control method (NFANN) is designed for stability control of a hydro-turbine system. Moreover, the more relaxed and simplified sufficient stability conditions are given as a new set of right-angle triangle membership function (RFANN), which has been guaranteed by strict mathematical derivation. The proposed methods tested on raw data of hydropower plant of Himreen Lake Dam. The optimal hydropower generation that observed is increased to the maximum over the actual value by PSO, SD-PSO, SD-FA, and FPSO increased by 1.5%, 2.3%, 3.1%, 2.5% respectively. The proposed SD-FA controller showed better and robustness compared to the other algorithm, which could resist the random disturbances.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Hammid, Ali Thaeer
author_facet	Hammid, Ali Thaeer
author_sort	Hammid, Ali Thaeer
title	Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
title_short	Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
title_full	Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
title_fullStr	Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
title_full_unstemmed	Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
title_sort	optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Electrical & Electronic Engineering
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23423/1/Optimization%20and%20control%20of%20hydro%20generation%20scheduling%20using%20hybrid%20firefly%20algorithm%20and%20particle%20swarm%20optimization%20techniques.pdf
_version_	1783732065748910080

Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques

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