### Abstract

This paper describes the implementation of a fast and easy-to-use, intelligence-based algorithm to assess the risk of voltage collapse when risk is defined as the product of the event likelihood and a severity function. In the event likelihood, the effect of weather is taken into account; the failure rate of each transmission line under different weather conditions is calculated using real historical outage data. A new severity function model that utilises the voltage collapse prediction index is proposed in this paper. Two intelligent techniques, i.e., support vector machines and a generalised regression neural network are studied, and their performances are evaluated using mean absolute and mean square error. The proposed methodology has been applied in a real power system network. Simulation results show that a generalized regression neural network provides the lowest mean absolute and mean square error.

Original language | English |
---|---|

Pages (from-to) | 1167-1179 |

Number of pages | 13 |

Journal | Australian Journal of Basic and Applied Sciences |

Volume | 5 |

Issue number | 6 |

Publication status | Published - Jun 2011 |

### Fingerprint

### Keywords

- GRNN
- Risk
- SVM
- Voltage collapse

### ASJC Scopus subject areas

- General

### Cite this

*Australian Journal of Basic and Applied Sciences*,

*5*(6), 1167-1179.

**Assessment of the risk of voltage collapse in a power system using intelligent techniques.** / Marsadek, Marayati; Mohamed, Azah; Mohd Nopiah, Zulkifli.

Research output: Contribution to journal › Article

*Australian Journal of Basic and Applied Sciences*, vol. 5, no. 6, pp. 1167-1179.

}

TY - JOUR

T1 - Assessment of the risk of voltage collapse in a power system using intelligent techniques

AU - Marsadek, Marayati

AU - Mohamed, Azah

AU - Mohd Nopiah, Zulkifli

PY - 2011/6

Y1 - 2011/6

N2 - This paper describes the implementation of a fast and easy-to-use, intelligence-based algorithm to assess the risk of voltage collapse when risk is defined as the product of the event likelihood and a severity function. In the event likelihood, the effect of weather is taken into account; the failure rate of each transmission line under different weather conditions is calculated using real historical outage data. A new severity function model that utilises the voltage collapse prediction index is proposed in this paper. Two intelligent techniques, i.e., support vector machines and a generalised regression neural network are studied, and their performances are evaluated using mean absolute and mean square error. The proposed methodology has been applied in a real power system network. Simulation results show that a generalized regression neural network provides the lowest mean absolute and mean square error.

AB - This paper describes the implementation of a fast and easy-to-use, intelligence-based algorithm to assess the risk of voltage collapse when risk is defined as the product of the event likelihood and a severity function. In the event likelihood, the effect of weather is taken into account; the failure rate of each transmission line under different weather conditions is calculated using real historical outage data. A new severity function model that utilises the voltage collapse prediction index is proposed in this paper. Two intelligent techniques, i.e., support vector machines and a generalised regression neural network are studied, and their performances are evaluated using mean absolute and mean square error. The proposed methodology has been applied in a real power system network. Simulation results show that a generalized regression neural network provides the lowest mean absolute and mean square error.

KW - GRNN

KW - Risk

KW - SVM

KW - Voltage collapse

UR - http://www.scopus.com/inward/record.url?scp=83355168081&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=83355168081&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:83355168081

VL - 5

SP - 1167

EP - 1179

JO - Australian Journal of Basic and Applied Sciences

JF - Australian Journal of Basic and Applied Sciences

SN - 1991-8178

IS - 6

ER -