Optimal economic dispatch for power generation using artificial neural network

This paper presents an optimal economic dispatch of electrical power plants by using back-propagation neural networks. The method of economic dispatch for generating units at different loads must have total fuel cost at the minimum point. Thcrc arc many conventional methods that can use to solve eco...

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Main Authors: S. Panta, S. Premrudeepreechacharn, S. Nuchprayoon, C. Dechthummarong, S. Janjommanit, S. Yachiangkam
Format: Conference Proceeding
Published: 2018
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Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=51349114625&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/61039
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-610392018-09-10T04:03:38Z Optimal economic dispatch for power generation using artificial neural network S. Panta S. Premrudeepreechacharn S. Nuchprayoon C. Dechthummarong S. Janjommanit S. Yachiangkam Energy Engineering This paper presents an optimal economic dispatch of electrical power plants by using back-propagation neural networks. The method of economic dispatch for generating units at different loads must have total fuel cost at the minimum point. Thcrc arc many conventional methods that can use to solve economic dispatch problem such as Lagrange multiplier method, Lamda iteration method and Newton-Raphson method. However, an obstacle in optimal economic dispatch of conventional methods is the changed load. They arc necessary to find thc optimal economic dispatch from time to time. Moreover, they need a lot of time to repeat calculation for a new solution again. This paper presents backpropagation neural networks model to carry out instead the conventional Lamda iteration method. It is compared with the experimental results of electrical power system of 3 10 and 20 generating units respectively. The testing results of the back-propagation neural networks are compared with the Lamda iteration method by testing the teaching data and non-teaching data. It shows clearly that the back-propagation neural networks can find out the solutions accurately and use time to calculate less than other systems that are tested. Error of prediction will be increased slightly by the number of generating units in electrical power plants because it needs to learn a lot of input and output data in the neural network dramatically. © 2007 RPS. 2018-09-10T04:03:10Z 2018-09-10T04:03:10Z 2007-12-01 Conference Proceeding 2-s2.0-51349114625 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=51349114625&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/61039
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Energy
Engineering
spellingShingle Energy
Engineering
S. Panta
S. Premrudeepreechacharn
S. Nuchprayoon
C. Dechthummarong
S. Janjommanit
S. Yachiangkam
Optimal economic dispatch for power generation using artificial neural network
description This paper presents an optimal economic dispatch of electrical power plants by using back-propagation neural networks. The method of economic dispatch for generating units at different loads must have total fuel cost at the minimum point. Thcrc arc many conventional methods that can use to solve economic dispatch problem such as Lagrange multiplier method, Lamda iteration method and Newton-Raphson method. However, an obstacle in optimal economic dispatch of conventional methods is the changed load. They arc necessary to find thc optimal economic dispatch from time to time. Moreover, they need a lot of time to repeat calculation for a new solution again. This paper presents backpropagation neural networks model to carry out instead the conventional Lamda iteration method. It is compared with the experimental results of electrical power system of 3 10 and 20 generating units respectively. The testing results of the back-propagation neural networks are compared with the Lamda iteration method by testing the teaching data and non-teaching data. It shows clearly that the back-propagation neural networks can find out the solutions accurately and use time to calculate less than other systems that are tested. Error of prediction will be increased slightly by the number of generating units in electrical power plants because it needs to learn a lot of input and output data in the neural network dramatically. © 2007 RPS.
format Conference Proceeding
author S. Panta
S. Premrudeepreechacharn
S. Nuchprayoon
C. Dechthummarong
S. Janjommanit
S. Yachiangkam
author_facet S. Panta
S. Premrudeepreechacharn
S. Nuchprayoon
C. Dechthummarong
S. Janjommanit
S. Yachiangkam
author_sort S. Panta
title Optimal economic dispatch for power generation using artificial neural network
title_short Optimal economic dispatch for power generation using artificial neural network
title_full Optimal economic dispatch for power generation using artificial neural network
title_fullStr Optimal economic dispatch for power generation using artificial neural network
title_full_unstemmed Optimal economic dispatch for power generation using artificial neural network
title_sort optimal economic dispatch for power generation using artificial neural network
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=51349114625&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/61039
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