文章基本信息

标题：Combination of Generator Capability Curve Constraint and Statistic-Fuzzy Load Clustering Algorithm to improve NN-OPF performance
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作者：Mat Syai’in ; Adi Soeprijanto
期刊名称：Journal of Electrical Systems
印刷版ISSN：1112-5209
出版年度：2012
卷号：8
期号：2
页码：198-208
出版社：ESRGroups
摘要：The inclusion of statistic-fuzzy Load clustering method in algorithm of NN-OPF is intended tomake the NN-OPF robust in load changing at a certain load range. Whereas, The inclusion ofGenerator Capability Curve (GCC) as a constraint in NN-OPF is to ensure cheap and safeoperation of generators. NN-OPF is built with reference to a Particle Swarm OptimizationOptimal Power Flow (PSO-OPF). There are three stages in Designing NN-OPF. The first stageis design of PSO-OPF with generator capability curve constraint. The second stage is loadclustering using statistic-fuzzy method. The third stage is training NN-OPF using constructiveback propagation method. In training process of Neural Network (NN), the nearness index ofload curve ( FWik ) resulting from statistic-fuzzy method, and total load (active power, reactivepower), are used as input. The pattern of generator scheduling resulting from PSO-OPF is usedas outputs. In this paper, the Java-Bali power system is used as sample system to verify thevalidity of this method. The simulation results using MATLAB software have shown that theproposed method has good performance. The proposed method is possible to apply in on-linesystem in normal condition, especially in representing non linear generation operation limitnear steady state stability limit and under excitation operation area.
关键词：Generator Capability Curve; Particle Swarm Optimization; Neural Network; Optimal;Power Flow; Statistic-Fuzzy.