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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| Format: | Conference or Workshop Item |
| Language: | en |
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2007
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| Online Access: | http://eprints.utm.my/7665/1/Mohd_Wazir_Mustafa_2007_Identification_of_Source_to_Sink_Relationship.pdf http://eprints.utm.my/7665/ http://ieeexplore.ieee.org/document/4509992/ |
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| _version_ | 1845471854398013440 |
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| 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 |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.utm.eprints-7665 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2007 |
| record_format | eprints |
| spelling | my.utm.eprints-76652017-08-29T02:34:46Z http://eprints.utm.my/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/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/ |
| 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 |
| title | 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_short | 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 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/7665/1/Mohd_Wazir_Mustafa_2007_Identification_of_Source_to_Sink_Relationship.pdf http://eprints.utm.my/7665/ http://ieeexplore.ieee.org/document/4509992/ |
| url_provider | http://eprints.utm.my/ |