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Transactions: WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS
Transactions ID Number: 53-572
Full Name: Shu-chen Wang
Position: Associate Professor
Age: ON
Sex: Female
Address: No. 212, Sec. 9, Yen-Ping N. Rd., Taipei
Country: TAIWAN
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E-mail address: scwang@mail.tcmt.edu.tw
Other E-mails: cjwu@mail.ntust.edu.tw
Title of the Paper: based on fuzzy c-means clustering for analysis of dynamic performance and coherency identification of generators in power system
Authors as they appear in the Paper: Shu-chen Wang
Email addresses of all the authors: scwang@mail.tcmt.edu.tw, cjwu@mail.ntust.edu.tw
Number of paper pages: 10
Abstract: This paper is to study the power system dynamic performance and the direct identification of coherent synchronous generators based on fuzzy c-means clustering. The main purpose of this paper is to analyze dynamic performance of power system and evaluate the influence of low frequency oscillation. And the application of fuzzy clustering approach is used to identify the coherent synchronous generators. In view of the conceptual appropriateness and computational simplicity, the fuzzy c-means give a fast and flexible method for clustering analysis. At first, the coherency measures are derived from the time-domain responses of generators to reveal the relations between any pair of generators. Then they are used as initial element values of the membership matrix in the fuzzy c-means clustering procedures. The use of coherency measures in the fuzzy c-means clustering can let the clustering procedures converge quickly. The schemes of various number of generator clusters ca!
n be procured. The comparison of the fuzzy c-means clustering with the similarity relation method is obtained in the simulation results. The approach in this paper needs less iterative times and can directly begin a clustering procedure for any number of clusters. The paper is investigated to envelope an effective evaluation of dynamic performance and the characteristic of dynamic stability and is demonstrated to show the effectiveness of this clustering approach.
Keywords: Coherency identification, Coherency measure, Fuzzy c-means, Cluster analysis, Power system, Dynamic performance
EXTENSION of the file: .pdf
Special (Invited) Session: Fuzzy Systems and Applications
Organizer of the Session: Nikos E. Mastorakis, Valeri Mladenov
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