Voltage stability prediction using artificial neural network for IEEE 69-bus and 30-bus / Nur Adha Hanif Mat Rahim
This project discusses the voltage stability prediction of a power system using Artificial Neural Network (ANN). Voltage instability is the one of the causes for a power system to breakdown. This incident is caused by severe low voltage condition which leads to blackouts to all system. The voltage s...
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| 語言: | English |
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2014
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my-uitm-ir.781222024-05-17T09:01:09Z Voltage stability prediction using artificial neural network for IEEE 69-bus and 30-bus / Nur Adha Hanif Mat Rahim 2014 Mat Rahim, Nur Adha Hanif Neural networks (Computer science) This project discusses the voltage stability prediction of a power system using Artificial Neural Network (ANN). Voltage instability is the one of the causes for a power system to breakdown. This incident is caused by severe low voltage condition which leads to blackouts to all system. The voltage stability prediction is essential in power system planning in order to prevent voltage collapse due to instability of voltage in power system. The voltage stability for each bus 1s determined by Voltage Stability Index (VSI). The value obtain from the VSI can determine the voltage stability for each bus in the system. There are two types of bus that will be use to determine the voltage stability prediction in power system which is 69-bus and 30-bus. A comparative study was conducted with Artificial Neural Network (ANN)-based prediction. 2014 Thesis https://ir.uitm.edu.my/id/eprint/78122/ https://ir.uitm.edu.my/id/eprint/78122/1/78122.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Mohd. Arshad, Pauziah |
| institution |
Universiti Teknologi MARA |
| collection |
UiTM Institutional Repository |
| language |
English |
| advisor |
Mohd. Arshad, Pauziah |
| topic |
Neural networks (Computer science) |
| spellingShingle |
Neural networks (Computer science) Mat Rahim, Nur Adha Hanif Voltage stability prediction using artificial neural network for IEEE 69-bus and 30-bus / Nur Adha Hanif Mat Rahim |
| description |
This project discusses the voltage stability prediction of a power system using Artificial Neural Network (ANN). Voltage instability is the one of the causes for a power system to breakdown. This incident is caused by severe low voltage condition which leads to blackouts to all system. The voltage stability prediction is essential in power system planning in order to prevent voltage collapse due to instability of voltage in power system. The voltage stability for each bus 1s determined by Voltage Stability Index (VSI). The value obtain from the VSI can determine the voltage stability for each bus in the system. There are two types of bus that will be use to determine the voltage stability prediction in power system which is 69-bus and 30-bus. A comparative study was conducted with Artificial Neural Network (ANN)-based prediction. |
| format |
Thesis |
| qualification_level |
Bachelor degree |
| author |
Mat Rahim, Nur Adha Hanif |
| author_facet |
Mat Rahim, Nur Adha Hanif |
| author_sort |
Mat Rahim, Nur Adha Hanif |
| title |
Voltage stability prediction using artificial neural network for IEEE 69-bus and 30-bus / Nur Adha Hanif Mat Rahim |
| title_short |
Voltage stability prediction using artificial neural network for IEEE 69-bus and 30-bus / Nur Adha Hanif Mat Rahim |
| title_full |
Voltage stability prediction using artificial neural network for IEEE 69-bus and 30-bus / Nur Adha Hanif Mat Rahim |
| title_fullStr |
Voltage stability prediction using artificial neural network for IEEE 69-bus and 30-bus / Nur Adha Hanif Mat Rahim |
| title_full_unstemmed |
Voltage stability prediction using artificial neural network for IEEE 69-bus and 30-bus / Nur Adha Hanif Mat Rahim |
| title_sort |
voltage stability prediction using artificial neural network for ieee 69-bus and 30-bus / nur adha hanif mat rahim |
| granting_institution |
Universiti Teknologi MARA (UiTM) |
| granting_department |
Faculty of Electrical Engineering |
| publishDate |
2014 |
| url |
https://ir.uitm.edu.my/id/eprint/78122/1/78122.pdf |
| _version_ |
1804889699004710912 |