### Abstract

This paper describes the implementation of a fast and accurate intelligent technique using generalized regression neural network to assess the risk of voltage collapse in power systems. The risk of voltage collapse is defined as the product of the probability of transmission line outage and its severity associated with voltage collapse. The effect of weather in the probability of transmission line outage is taken into account in which the failure rate of each transmission line with respect to weather conditions is calculated. A new severity function model that utilises the voltage collapse prediction index is also considered in this assessment method. The performance of the generalised regression neural network is evaluated using mean absolute and mean square errors. The proposed risk based voltage collapse assessment method has been validated on a real power system.

Original language | English |
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Title of host publication | Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011 |

DOIs | |

Publication status | Published - 18 Oct 2011 |

Event | 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011 - Bandung, Indonesia Duration: 17 Jul 2011 → 19 Jul 2011 |

### Publication series

Name | Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011 |
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### Other

Other | 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011 |
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Country | Indonesia |

City | Bandung |

Period | 17/07/11 → 19/07/11 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Information Systems
- Electrical and Electronic Engineering

### Cite this

*Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011*[6021767] (Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011). https://doi.org/10.1109/ICEEI.2011.6021767

}

*Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011.*, 6021767, Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011, 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011, Bandung, Indonesia, 17/07/11. https://doi.org/10.1109/ICEEI.2011.6021767

**Risk-based voltage collapse assessment using generalized regression neural network.** / Marsadek, Marayati; Mohamed, Azah; Nopiah, Zulkifli Mohd.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Risk-based voltage collapse assessment using generalized regression neural network

AU - Marsadek, Marayati

AU - Mohamed, Azah

AU - Nopiah, Zulkifli Mohd

PY - 2011/10/18

Y1 - 2011/10/18

N2 - This paper describes the implementation of a fast and accurate intelligent technique using generalized regression neural network to assess the risk of voltage collapse in power systems. The risk of voltage collapse is defined as the product of the probability of transmission line outage and its severity associated with voltage collapse. The effect of weather in the probability of transmission line outage is taken into account in which the failure rate of each transmission line with respect to weather conditions is calculated. A new severity function model that utilises the voltage collapse prediction index is also considered in this assessment method. The performance of the generalised regression neural network is evaluated using mean absolute and mean square errors. The proposed risk based voltage collapse assessment method has been validated on a real power system.

AB - This paper describes the implementation of a fast and accurate intelligent technique using generalized regression neural network to assess the risk of voltage collapse in power systems. The risk of voltage collapse is defined as the product of the probability of transmission line outage and its severity associated with voltage collapse. The effect of weather in the probability of transmission line outage is taken into account in which the failure rate of each transmission line with respect to weather conditions is calculated. A new severity function model that utilises the voltage collapse prediction index is also considered in this assessment method. The performance of the generalised regression neural network is evaluated using mean absolute and mean square errors. The proposed risk based voltage collapse assessment method has been validated on a real power system.

UR - http://www.scopus.com/inward/record.url?scp=80054027939&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80054027939&partnerID=8YFLogxK

U2 - 10.1109/ICEEI.2011.6021767

DO - 10.1109/ICEEI.2011.6021767

M3 - Conference contribution

AN - SCOPUS:80054027939

SN - 9781457707520

T3 - Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011

BT - Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011

ER -