Volume 7, Issue 1 (March 2011)                   IJEEE 2011, 7(1): 33-41 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Aghamohammadi M, Hashemi S S, Ghazizadeh M S. Improving Voltage Stability Margin Using Voltage Profile and Sensitivity Analysis by Neural Network. IJEEE 2011; 7 (1) :33-41
URL: http://ijeee.iust.ac.ir/article-1-281-en.html
Improving Voltage Stability Margin Using Voltage Profile and Sensitivity Analysis by Neural Network
M. Aghamohammadi , S. S. Hashemi , M. S. Ghazizadeh
Abstract:   (11746 Views)
This paper presents a new approach for estimating and improving voltage stability margin from phase and magnitude profile of bus voltages using sensitivity analysis of Voltage Stability Assessment Neural Network (VSANN). Bus voltage profile contains useful information about system stability margin including the effect of load-generation, line outage and reactive power compensation so, it is adopted as input pattern for VSANN. In fact, VSANN establishes a functionality for VSM with respect to voltage profile. Sensitivity analysis of VSM with respect to voltage profile and reactive power compensation extracted from information stored in the weighting factor of VSANN, is the most dominant feature of the proposed approach. Sensitivity of VSM helps one to select most effective buses for reactive power compensation aimed enhancing VSM. The proposed approach has been applied on IEEE 39-bus test system which demonstrated applicability of the proposed approach.
Keywords: Voltage Stability Margin , Voltage Profile , Feature Extraction , Neural Networks , Sensitivity Analysis
Full-Text [PDF 935 kb]   (10004 Downloads)    
Type of Study: Research Paper | Subject: Static and Dynamic Security Assessment
Received: 2010/05/11 | Revised: 2011/12/24 | Accepted: 2013/12/30
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.