Implementation of adaptive neuro fuzzy inference system and back propagation neural network for the appraisal of power system contingency analysis
1 Department of Electrical and Electronic Engineering, University of Abuja, Nigeria.
2 Department of Electrical and Electronic Engineering, Federal polytechnics, Daura.
3 Department of Electrical and Electronic Engineering, Baze University Abuja, Nigeria.
4 Management Information System Unit, Yusuf Maitama Sule University, Kano-Nigeria.
5 Department of Electrical Engineering, Kano University of Science and Technology Wudil, Kano-Nigeria.
GSC Advanced Engineering and Technology, 2022, 03(01), 016–028.
Article DOI: 10.30574/gscaet.2022.3.1.0024
Publication history:
Received on 01 January 2022; revised on 15 February 2022; accepted on 17 February 2022
Abstract:
Power System Security and Contingency analysis is one of the most important tasks in power systems. In operation, contingency analysis assists engineers to operate the power system at a secure and safe operating point where equipment are loaded within their safe operating area (SOA). Power is dispatched to customers with acceptable quality standards. The results of off-line load flow calculations are used to estimate performance indices (PI flow, PI V). MATLAB toolbox was the proposed methodology used for the implementation. The proposed approach for contingency analysis was found to be appropriate for screening and ranking fast voltage and line flow contingencies.
Keywords:
Contingency analysis; Evaluation; Screening; Ranking; Adaptive neuro-fuzzy interference system (ANFIS); Performance Index; Voltage and flow ranking
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
