Application of artificial neural network for voltage stability assessment / Idris Harun
This report presents an application of Artificial Neural Network model for prediction of voltage stability condition in power system network. Voltage stability analysis involves the determination of stability factor, i.e. L-factor. The ANN by using the Back-Propagation method was selected. The ANN m...
محفوظ في:
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| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2003
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| الوصول للمادة أونلاين: | https://ir.uitm.edu.my/id/eprint/77858/1/77858.pdf |
| الوسوم: |
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my-uitm-ir.77858 |
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my-uitm-ir.778582023-07-09T09:17:00Z Application of artificial neural network for voltage stability assessment / Idris Harun 2003 Harun, Idris Back propagation (Artificial intelligence) This report presents an application of Artificial Neural Network model for prediction of voltage stability condition in power system network. Voltage stability analysis involves the determination of stability factor, i.e. L-factor. The ANN by using the Back-Propagation method was selected. The ANN model developed has three layers i.e. input layer, hidden layer and output layer. The same sets of data have used in the training and the same other sets of data for testing process. AU those sets of data were obtained by the Load Flow programme. Real, reactive power, Vload and Oload have been used as input nodes and L-factor values as output node. Tests were carried out and the results were compared on the basic of learning rate, momentum, number of hidden node and iteration. From the results, it shows that the artificial neural network can be used to predict the level of voltage stability condition. 2003 Thesis https://ir.uitm.edu.my/id/eprint/77858/ https://ir.uitm.edu.my/id/eprint/77858/1/77858.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Abdul Rahman, Titik Khawa |
| institution |
Universiti Teknologi MARA |
| collection |
UiTM Institutional Repository |
| language |
English |
| advisor |
Abdul Rahman, Titik Khawa |
| topic |
Back propagation (Artificial intelligence) |
| spellingShingle |
Back propagation (Artificial intelligence) Harun, Idris Application of artificial neural network for voltage stability assessment / Idris Harun |
| description |
This report presents an application of Artificial Neural Network model for prediction of voltage stability condition in power system network. Voltage stability analysis involves the determination of stability factor, i.e. L-factor. The ANN by using the Back-Propagation method was selected. The ANN model developed has three layers i.e. input layer, hidden layer and output layer. The same sets of data have used in the training and the same other sets of data for testing process. AU those sets of data were obtained by the Load Flow programme. Real, reactive power, Vload and Oload have been used as input nodes and L-factor values as output node. Tests were carried out and the results were compared on the basic of learning rate, momentum, number of hidden node and iteration. From the results, it shows that the artificial neural network can be used to predict the level of voltage stability condition. |
| format |
Thesis |
| qualification_level |
Bachelor degree |
| author |
Harun, Idris |
| author_facet |
Harun, Idris |
| author_sort |
Harun, Idris |
| title |
Application of artificial neural network for voltage stability assessment / Idris Harun |
| title_short |
Application of artificial neural network for voltage stability assessment / Idris Harun |
| title_full |
Application of artificial neural network for voltage stability assessment / Idris Harun |
| title_fullStr |
Application of artificial neural network for voltage stability assessment / Idris Harun |
| title_full_unstemmed |
Application of artificial neural network for voltage stability assessment / Idris Harun |
| title_sort |
application of artificial neural network for voltage stability assessment / idris harun |
| granting_institution |
Universiti Teknologi MARA (UiTM) |
| granting_department |
Faculty of Electrical Engineering |
| publishDate |
2003 |
| url |
https://ir.uitm.edu.my/id/eprint/77858/1/77858.pdf |
| _version_ |
1783736174676803584 |