Volume 7, Issue 4 (December 2011)                   IJEEE 2011, 7(4): 273-282 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
S. Javan D, Rajabi Mashhadi H. Fast Voltage and Power Flow Contingency Ranking Using Enhanced Radial Basis Function Neural Network. IJEEE 2011; 7 (4) :273-282
URL: http://ijeee.iust.ac.ir/article-1-384-en.html
Fast Voltage and Power Flow Contingency Ranking Using Enhanced Radial Basis Function Neural Network
D. S. Javan , H. Rajabi Mashhadi
Abstract:   (8425 Views)
Deregulation of power system in recent years has changed static security assessment to the major concerns for which fast and accurate evaluation methodology is needed. Contingencies related to voltage violations and power line overloading have been responsible for power system collapse. This paper presents an enhanced radial basis function neural network (RBFNN) approach for on-line ranking of the contingencies expected to cause steady state bus voltage and power flow violations. Hidden layer units (neurons) have been selected with the growing and pruning algorithm which has the superiority of being able to choose optimal unit’s center and width (radius). A feature preference technique-based class separability index and correlation coefficient has been employed to identify the relevant inputs for the neural network. The advantages of this method are simplicity of algorithm and high accuracy in classification. The effectiveness of the proposed approach has been demonstrated on IEEE 14-bus power system.
Keywords: Static Security Assessment , Neural Network , Feature Selection , Contingency , Performance Index
Full-Text [PDF 1285 kb]   (4826 Downloads)    
Type of Study: Research Paper | Subject: Static and Dynamic Security Assessment
Received: 2011/04/04 | Revised: 2011/12/24 | Accepted: 2011/07/18
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.