Application of SOM-Based Fuzzy Systems in Voltage Security Margin Estimation
Author(s) -
MuChun Su,
Eugene Lai,
Chee-Yuen Tew,
ChihWen Liu,
Chen-Sung Chang
Publication year - 2001
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2001.p0157
Subject(s) - computer science , self organizing map , margin (machine learning) , feature vector , fuzzy logic , vector quantization , data mining , voltage , feature (linguistics) , electric power system , artificial intelligence , quantization (signal processing) , cluster analysis , pattern recognition (psychology) , algorithm , machine learning , power (physics) , linguistics , philosophy , physics , quantum mechanics
In recent years, many significant research efforts have been devoted to voltage security margins which show how close the current operating point of a power system is to a voltage collapse point as assessment of voltage security. In this paper we propose a technique based on the SOM-based fuzzy systems for voltage security margin estimation. The SOM-based fuzzy systems use the Kohonen’s self-organizing feature map (SOM) algorithm, not only for its vector quantization feature, but also for its topological property. The vector quantization feature of feature maps is used to search a good supply of most representative cluster centers. Then the topology-preserving feature is fully utilized to select a set of most influential rules so as to contribute to the computation of system outputs. The proposed technique was tested on 1604 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficiency.
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