Application of ANN to real and reactive power allocation scheme
This chapter describes the implementation of ANN for real and reactive power transfer allocation. The 25 bus equivalent power system of south Malaysia region and IEEE 118 bus system are used to demonstrate the applicability of the ANN output compared to that of the Modified Nodal Equations (MNE) whi...
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my.utm.453262017-08-10T03:40:43Z http://eprints.utm.my/id/eprint/45326/ Application of ANN to real and reactive power allocation scheme Abd. Khalid, Saifulnizam Mustafa, Mohd Wazir Shareef, Hussain Khairuddin, Azhar TK Electrical engineering. Electronics Nuclear engineering This chapter describes the implementation of ANN for real and reactive power transfer allocation. The 25 bus equivalent power system of south Malaysia region and IEEE 118 bus system are used to demonstrate the applicability of the ANN output compared to that of the Modified Nodal Equations (MNE) which is used as trainers for real and reactive power allocation. The basic idea is to use supervised learning paradigm to train the ANN. Then the descriptions of inputs and outputs of the training data for the ANN are easily obtained from the load flow results and each method used as teachers respectively. The proposed ANN based method provides promising results in terms of accuracy and computation time. Artificial intelligence has been proven to be able to solve complex processes in deregulated power system such as loss allocation. So, it can be expected that the developed methodology will further contribute in improving the computation time of transmission usage allocation for deregulated system. Intech 2011 Book Section PeerReviewed Abd. Khalid, Saifulnizam and Mustafa, Mohd Wazir and Shareef, Hussain and Khairuddin, Azhar (2011) Application of ANN to real and reactive power allocation scheme. In: Artificial Neural Network Industrial and Control Engineering Applications. Intech, Rijeka, Croatia, pp. 283-305. ISBN 978-953-307-220-3 |
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TK Electrical engineering. Electronics Nuclear engineering Abd. Khalid, Saifulnizam Mustafa, Mohd Wazir Shareef, Hussain Khairuddin, Azhar Application of ANN to real and reactive power allocation scheme |
| description |
This chapter describes the implementation of ANN for real and reactive power transfer allocation. The 25 bus equivalent power system of south Malaysia region and IEEE 118 bus system are used to demonstrate the applicability of the ANN output compared to that of the Modified Nodal Equations (MNE) which is used as trainers for real and reactive power allocation. The basic idea is to use supervised learning paradigm to train the ANN. Then the descriptions of inputs and outputs of the training data for the ANN are easily obtained from the load flow results and each method used as teachers respectively. The proposed ANN based method provides promising results in terms of accuracy and computation time. Artificial intelligence has been proven to be able to solve complex processes in deregulated power system such as loss allocation. So, it can be expected that the developed methodology will further contribute in improving the computation time of transmission usage allocation for deregulated system. |
| format |
Book Section |
| author |
Abd. Khalid, Saifulnizam Mustafa, Mohd Wazir Shareef, Hussain Khairuddin, Azhar |
| author_facet |
Abd. Khalid, Saifulnizam Mustafa, Mohd Wazir Shareef, Hussain Khairuddin, Azhar |
| author_sort |
Abd. Khalid, Saifulnizam |
| title |
Application of ANN to real and reactive power allocation scheme |
| title_short |
Application of ANN to real and reactive power allocation scheme |
| title_full |
Application of ANN to real and reactive power allocation scheme |
| title_fullStr |
Application of ANN to real and reactive power allocation scheme |
| title_full_unstemmed |
Application of ANN to real and reactive power allocation scheme |
| title_sort |
application of ann to real and reactive power allocation scheme |
| publisher |
Intech |
| publishDate |
2011 |
| url |
http://eprints.utm.my/id/eprint/45326/ |
| _version_ |
1643651709505896448 |
| score |
13.250246 |