Sensitivity characteristic of neural network as a tool for analyzing and improving voltage stability

Mohammad Reza Aghamohammadi, Moshen Mohammadian, Hiroumi Saitoh

Research output: Contribution to conferencePaperpeer-review

21 Citations (Scopus)

Abstract

This paper proposes a new approach for analyzing and improving voltage stability of power systems based on application of neural network technique and its sensitivity characteristic. The stability considered in this paper, is static voltage stability and the stability index, which is used, is system loadability limit associated with the point of voltage stability limit. In order to have a fast evaluation of power system voltage stability limit and also be able to perform sensitivity analysis of this limit with respect to system variables, a neural network is adopted and trained to construct a functional relationship between system variables and loadability limit. If neural network evaluates voltage stability limit with a low security margin then it becomes necessary to improve voltage stability. For this purpose, output/input sensitivity characteristic of the neural network is used, to evaluate the sensitivity of voltage stability limit with respect to system variables like reactive power generation. Then, this sensitivity is used as a guideline to modify reactive power generation to increase voltage stability limit. The proposed approach is tested on IEEE 14-Bus system with promising results.

Original languageEnglish
Pages1128-1132
Number of pages5
Publication statusPublished - 2002 Dec 1
Externally publishedYes
EventIEEE/PES Transmission and Distribution Conference and Exhibition 2002 : Asia Pacific - Yokahama, Japan
Duration: 2002 Oct 62002 Oct 10

Other

OtherIEEE/PES Transmission and Distribution Conference and Exhibition 2002 : Asia Pacific
CountryJapan
CityYokahama
Period02/10/602/10/10

Keywords

  • Neural Network
  • Reactive Power
  • Security Assessment
  • Security Margin
  • Sensitivity Characteristic
  • Voltage Stability

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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