Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply

Artificial Neural Networks (ANNs) are general purpose optimization techniques based on principles inspired from biological neurons in the brain which consists of a number of simple and highly interconnected processors (neurons). ANN has proved to be able to solve optimization problems in power sy...

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Main Author:	Raja Zolkiply, Raja Ezham Shariffudin
Format:	Student Project
Published:	Faculty of Information Technology and Quantitative Sciences 2003
Online Access:	http://ir.uitm.edu.my/id/eprint/1985/

id	uitm-1985
recordtype	eprints
spelling	uitm-19852017-04-19T09:13:46Z http://ir.uitm.edu.my/id/eprint/1985/ Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply Raja Zolkiply, Raja Ezham Shariffudin Artificial Neural Networks (ANNs) are general purpose optimization techniques based on principles inspired from biological neurons in the brain which consists of a number of simple and highly interconnected processors (neurons). ANN has proved to be able to solve optimization problems in power system. One of the areas in power system operation that requires optimal solution is the Unit Commitment (UC) problem. The UC problem involves determination of start-up and shut-down schedule of generating units, and indirectly determines the optimum power should be generated by each unit committed over a period of time to meet the forecasted load demand at minimum cost. Besides that, the commitment schedule must satisfy other constraints in order to minimize the total production cost. Therefore, this constitutes a problem to the operators, where they find it difficult to make the decision manually on which unit to keep online, and which unit to switch to offline, in order to minimize the production cost. The ANN approach has proved to be able to solve the UC problem but involve several problems such as divergence, excessive computation time, too much iteration for solving small task and so on. In order to improve the implementation of ANN for solving UC problem in power systems, this paper presents a comparison study between standard backpropagation algorithm, extended backpropagation algorithms and hybrid approach. The ANN used to forecast the power of four generating units in a small power system consists of multilayer neural network which consists of three input nodes, several hidden nodes and five output nodes. Besides that, input parameters involve are current stage of load demand (first stage to sixth stage), current load demand (megawatt) and previous load demand (megawatt), while the outputs of the neural netwoiic are power generated by four thermal units at current stage and total cost. Faculty of Information Technology and Quantitative Sciences 2003 Student Project NonPeerReviewed Raja Zolkiply, Raja Ezham Shariffudin (2003) Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply. [Student Project] (Submitted)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
description	Artificial Neural Networks (ANNs) are general purpose optimization techniques based on principles inspired from biological neurons in the brain which consists of a number of simple and highly interconnected processors (neurons). ANN has proved to be able to solve optimization problems in power system. One of the areas in power system operation that requires optimal solution is the Unit Commitment (UC) problem. The UC problem involves determination of start-up and shut-down schedule of generating units, and indirectly determines the optimum power should be generated by each unit committed over a period of time to meet the forecasted load demand at minimum cost. Besides that, the commitment schedule must satisfy other constraints in order to minimize the total production cost. Therefore, this constitutes a problem to the operators, where they find it difficult to make the decision manually on which unit to keep online, and which unit to switch to offline, in order to minimize the production cost. The ANN approach has proved to be able to solve the UC problem but involve several problems such as divergence, excessive computation time, too much iteration for solving small task and so on. In order to improve the implementation of ANN for solving UC problem in power systems, this paper presents a comparison study between standard backpropagation algorithm, extended backpropagation algorithms and hybrid approach. The ANN used to forecast the power of four generating units in a small power system consists of multilayer neural network which consists of three input nodes, several hidden nodes and five output nodes. Besides that, input parameters involve are current stage of load demand (first stage to sixth stage), current load demand (megawatt) and previous load demand (megawatt), while the outputs of the neural netwoiic are power generated by four thermal units at current stage and total cost.
format	Student Project
author	Raja Zolkiply, Raja Ezham Shariffudin
spellingShingle	Raja Zolkiply, Raja Ezham Shariffudin Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply
author_facet	Raja Zolkiply, Raja Ezham Shariffudin
author_sort	Raja Zolkiply, Raja Ezham Shariffudin
title	Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply
title_short	Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply
title_full	Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply
title_fullStr	Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply
title_full_unstemmed	Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply
title_sort	application of artificial neural network for solving unit commitment problem / raja ezham shariffudin raja zolkiply
publisher	Faculty of Information Technology and Quantitative Sciences
publishDate	2003
url	http://ir.uitm.edu.my/id/eprint/1985/
first_indexed	2023-09-18T22:46:25Z
last_indexed	2023-09-18T22:46:25Z
_version_	1777417254896402432

Application of artificial neural network for solving unit commitment problem / Raja Ezham Shariffudin Raja Zolkiply

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