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Identification of source to sink relationship in deregulated power systems using artificial neural network

This paper suggests a method to identify the relationship of real power transfer between source and sink using artificial neural network (ANN). The basic idea is to use supervised learning paradigm to train the ANN. For that a conventional power flow tracing method is used as a teacher. Based on sol...

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主要な著者: Mustafa, Mohd. Wazir, Khairuddin, Azhar, Shareef, Hussain, Khalid, S. N.
フォーマット: Conference or Workshop Item
言語:English
出版事項: 2007
主題:
TK Electrical engineering. Electronics Nuclear engineering
オンライン･アクセス:http://eprints.utm.my/id/eprint/7665/1/Mohd_Wazir_Mustafa_2007_Identification_of_Source_to_Sink_Relationship.pdf
http://eprints.utm.my/id/eprint/7665/
http://ieeexplore.ieee.org/document/4509992/
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spelling my.utm.76652017-08-29T02:34:46Z http://eprints.utm.my/id/eprint/7665/ Identification of source to sink relationship in deregulated power systems using artificial neural network Mustafa, Mohd. Wazir Khairuddin, Azhar Shareef, Hussain Khalid, S. N. TK Electrical engineering. Electronics Nuclear engineering This paper suggests a method to identify the relationship of real power transfer between source and sink using artificial neural network (ANN). The basic idea is to use supervised learning paradigm to train the ANN. For that a conventional power flow tracing method is used as a teacher. Based on solved load flow and followed by power tracing procedure, the description of inputs and outputs of the training data for the ANN is easily obtained. An artificial neural network is developed to assess which generators are supplying a specific load. Most commonly used feedforward architecture has been chosen for the proposed ANN power transfer allocation technique. Almost all system variables obtained from load flow solutions are utilised as an input to the neural network. Moreover, log-sigmoid activation functions are incorporated in the hidden layer to realise the non linear nature of the power flow allocation. The proposed ANN provides promising results in terms of accuracy and computation time. The IEEE 14-bus network is utilised as a test system to illustrate the effectiveness of the ANN output compared to that of conventional methods. 2007-12 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/7665/1/Mohd_Wazir_Mustafa_2007_Identification_of_Source_to_Sink_Relationship.pdf Mustafa, Mohd. Wazir and Khairuddin, Azhar and Shareef, Hussain and Khalid, S. N. (2007) Identification of source to sink relationship in deregulated power systems using artificial neural network. In: Power Engineering Conference, 2007. IPEC 2007. International, 3-6 Dec 2007, Singapore. http://ieeexplore.ieee.org/document/4509992/
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Mustafa, Mohd. Wazir
Khairuddin, Azhar
Shareef, Hussain
Khalid, S. N.
Identification of source to sink relationship in deregulated power systems using artificial neural network
description This paper suggests a method to identify the relationship of real power transfer between source and sink using artificial neural network (ANN). The basic idea is to use supervised learning paradigm to train the ANN. For that a conventional power flow tracing method is used as a teacher. Based on solved load flow and followed by power tracing procedure, the description of inputs and outputs of the training data for the ANN is easily obtained. An artificial neural network is developed to assess which generators are supplying a specific load. Most commonly used feedforward architecture has been chosen for the proposed ANN power transfer allocation technique. Almost all system variables obtained from load flow solutions are utilised as an input to the neural network. Moreover, log-sigmoid activation functions are incorporated in the hidden layer to realise the non linear nature of the power flow allocation. The proposed ANN provides promising results in terms of accuracy and computation time. The IEEE 14-bus network is utilised as a test system to illustrate the effectiveness of the ANN output compared to that of conventional methods.
format Conference or Workshop Item
author Mustafa, Mohd. Wazir
Khairuddin, Azhar
Shareef, Hussain
Khalid, S. N.
author_facet Mustafa, Mohd. Wazir
Khairuddin, Azhar
Shareef, Hussain
Khalid, S. N.
author_sort Mustafa, Mohd. Wazir
title Identification of source to sink relationship in deregulated power systems using artificial neural network
title_short Identification of source to sink relationship in deregulated power systems using artificial neural network
title_full Identification of source to sink relationship in deregulated power systems using artificial neural network
title_fullStr Identification of source to sink relationship in deregulated power systems using artificial neural network
title_full_unstemmed Identification of source to sink relationship in deregulated power systems using artificial neural network
title_sort identification of source to sink relationship in deregulated power systems using artificial neural network
publishDate 2007
url http://eprints.utm.my/id/eprint/7665/1/Mohd_Wazir_Mustafa_2007_Identification_of_Source_to_Sink_Relationship.pdf
http://eprints.utm.my/id/eprint/7665/
http://ieeexplore.ieee.org/document/4509992/
_version_ 1643644824625086464
score 13.251813

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