Samuel, I. A. and Katende, J. and Awosope, C. O. A. and Awelewa, A. A.
(2017)
*Prediction of Voltage Collapse in Electrical Power System Networks using a New Voltage Stability Index.*
International Journal of Applied Engineering Research, 12 (2).
pp. 190-199.
ISSN 0973-4562

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## Abstract

The numerous power system blackouts in the past decade and in recent times attest to the fact that more work still needs to be done to tackle the problem of voltage instability and the resultant voltage collapse. This research work proposes a new line stability index that is suitable for the prediction of voltage collapse in Power System Networks (PSNs). This index code-named the New Line Stability Index-1 (NLSI_1) was obtained by deriving from first principles equivalent expressions for the Line Stability Index (Lmn) and the Fast Voltage Stability Index (FVSI) and combining them through a switching logic based on the voltage angle difference since it can signal the imminence of voltage collapse. This new index (NLSI_1) was tested on the IEEE 14-bus system and it gives the same results as the other indices (Lmn and FVSI). For the base case, the IEEE 14-bus test system was found to be stable with all the three indices having approximately equal values (< 1) for all the lines. The contingency case reveals that bus 14 ranks as the weakest bus in the system with the smallest maximum permissible reactive load of 74.6 Mvar and the critical line with respect to bus 14, is the line connecting bus 13 to bus 14. The values of the three indices, Lmn, FVSI and NSLI_1, are approximately equal thereby further validating the accuracy of the new line stability index-1 (NLSI_1).

Item Type: | Article |
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Uncontrolled Keywords: | voltage stability, voltage stability indices, new line stability index, voltage collapse, Lmn, FVSI, NSLI_1; |

Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |

Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |

Depositing User: | Mrs Hannah Akinwumi |

Date Deposited: | 16 Jun 2017 09:50 |

Last Modified: | 16 Jun 2017 09:50 |

URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/8255 |

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