Voltage Collapse Prediction Using Artificial Neural Network
Voltage instability is a serious condition that can occur in a power system. An imbalance in reactive power, inadequate utilization of voltage control devices, loss of a component or an abrupt rise in load demand can cause this entire disturbance which leads a system to blackout, either partial or c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/46/1/24 |
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| author | Atiqa Asif Ayesha Ijaz Ayesha Urooj Taskeen Khan Abdullah Munir |
| author_facet | Atiqa Asif Ayesha Ijaz Ayesha Urooj Taskeen Khan Abdullah Munir |
| author_sort | Atiqa Asif |
| collection | DOAJ |
| description | Voltage instability is a serious condition that can occur in a power system. An imbalance in reactive power, inadequate utilization of voltage control devices, loss of a component or an abrupt rise in load demand can cause this entire disturbance which leads a system to blackout, either partial or complete. In order to avoid the condition of voltage collapse, we need to predict the state of buses in the system so that we can prevent the occurrence of major outages. This research puts forward two methods for voltage collapse prediction. The first one is to compute a new line stability index (NLSI_1) through an artificial neural network, and the other one is to present a normalized power change index (NPCI) for the prediction. These indices are applied and examined on the IEEE-14 bus system; they check the state of the buses and tell us about the stability of the system. A detailed methodology and explanation are given in the following sections. According to the neural network outcomes, the normalized power change index (NPCI) proves to be more accurate than NLSI_1 for the test system. |
| first_indexed | 2024-03-08T20:47:34Z |
| format | Article |
| id | doaj.art-2f4f7933600d42c38290b83727e6c93d |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-03-08T20:47:34Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-2f4f7933600d42c38290b83727e6c93d2023-12-22T14:07:06ZengMDPI AGEngineering Proceedings2673-45912023-09-014612410.3390/engproc2023046024Voltage Collapse Prediction Using Artificial Neural NetworkAtiqa Asif0Ayesha Ijaz1Ayesha Urooj2Taskeen Khan3Abdullah Munir4Department of Electrical Engineering, N.E.D University of Engineering & Technology, Karachi 75270, PakistanDepartment of Electrical Engineering, N.E.D University of Engineering & Technology, Karachi 75270, PakistanDepartment of Electrical Engineering, N.E.D University of Engineering & Technology, Karachi 75270, PakistanDepartment of Electrical Engineering, N.E.D University of Engineering & Technology, Karachi 75270, PakistanDepartment of Electrical Engineering, N.E.D University of Engineering & Technology, Karachi 75270, PakistanVoltage instability is a serious condition that can occur in a power system. An imbalance in reactive power, inadequate utilization of voltage control devices, loss of a component or an abrupt rise in load demand can cause this entire disturbance which leads a system to blackout, either partial or complete. In order to avoid the condition of voltage collapse, we need to predict the state of buses in the system so that we can prevent the occurrence of major outages. This research puts forward two methods for voltage collapse prediction. The first one is to compute a new line stability index (NLSI_1) through an artificial neural network, and the other one is to present a normalized power change index (NPCI) for the prediction. These indices are applied and examined on the IEEE-14 bus system; they check the state of the buses and tell us about the stability of the system. A detailed methodology and explanation are given in the following sections. According to the neural network outcomes, the normalized power change index (NPCI) proves to be more accurate than NLSI_1 for the test system.https://www.mdpi.com/2673-4591/46/1/24artificial neural networkvoltage stability indicesvoltage collapse |
| spellingShingle | Atiqa Asif Ayesha Ijaz Ayesha Urooj Taskeen Khan Abdullah Munir Voltage Collapse Prediction Using Artificial Neural Network Engineering Proceedings artificial neural network voltage stability indices voltage collapse |
| title | Voltage Collapse Prediction Using Artificial Neural Network |
| title_full | Voltage Collapse Prediction Using Artificial Neural Network |
| title_fullStr | Voltage Collapse Prediction Using Artificial Neural Network |
| title_full_unstemmed | Voltage Collapse Prediction Using Artificial Neural Network |
| title_short | Voltage Collapse Prediction Using Artificial Neural Network |
| title_sort | voltage collapse prediction using artificial neural network |
| topic | artificial neural network voltage stability indices voltage collapse |
| url | https://www.mdpi.com/2673-4591/46/1/24 |
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