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Performance Evaluation of Deregulated Power System Static Security Assessment using RBF-NN Technique
Author(s) -
Ibrahim Saeh,
Mohd Wazir Mustafa
Publication year - 2013
Publication title -
jurnal teknologi
Language(s) - English
Resource type - Journals
eISSN - 2180-3722
pISSN - 0127-9696
DOI - 10.11113/jt.v64.1841
Subject(s) - computer science , classifier (uml) , electric power system , database transaction , machine learning , selection (genetic algorithm) , artificial intelligence , reliability engineering , data mining , power (physics) , engineering , physics , quantum mechanics , programming language
This paper proposes RBF-NN for classification and performance evaluation of static security assessment in deregulated power system. This study suggests an attribute selection and classification algorithms for static security evaluation (SSE) and its impact is proposed. For the base case, pure pool dispatch (with no bilateral transactions) and bilateral transaction comparisons are discussed on IEEE57- bus system. In this paper, a comprehensive comparison of AI classifiers to examine whether the power system is secured under steady-state operating conditions is presented. The proposed classifier is implemented on a 30 and 57 IEEE test system. To assess the actual overall performance regarding studying techniques, this research proposes performance evaluation schemes vis CCR, TPR and TNR and implemented on various IEEE test systems. The simulation results have shown the powerfulness of the proposed method as compare to another proposed AI classifiers.

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