Application of Fuzzy Clustering Technique to Reduce the Load Data in Reliability Evaluation of Restructured Power Systems

Author

Viswanath, P.A. ; Goel, L. ; Wang, P.

Author_Institution

Sch. of Electr. Eng. & Comput. Sci., Univ. of Newcastle, Newcastle, NSW, Australia

fYear

2009

fDate

16-18 Dec. 2009

Firstpage

543

Lastpage

548

Abstract

This paper presents the application of fuzzy clustering technique on large load data to greatly reduce calculations in reliability evaluation of restructured power systems. The method involves: first grouping a large load data into few clusters, secondly calculating partial membership value of each load point in each cluster, thirdly calculating reliability indices for each cluster and finally, expressing the reliability indices at each load point in terms of the reliability index of the cluster and the membership value that the load point has in that cluster. A non-sequential Monte Carlo simulation technique based on this framework has been proposed to evaluate the customer reliability of restructured power systems.

Keywords

Monte Carlo methods; fuzzy set theory; pattern clustering; power markets; power system reliability; customer reliability; fuzzy clustering technique; load data; nonsequential Monte Carlo simulation technique; power system reliability; reliability evaluation; restructured power systems; Clustering algorithms; Contingency management; Fuzzy systems; Hybrid power systems; ISO; Power generation; Power system analysis computing; Power system modeling; Power system reliability; Sampling methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Emerging Trends in Engineering and Technology (ICETET), 2009 2nd International Conference on

Conference_Location

Nagpur

Print_ISBN

978-1-4244-5250-7

Electronic_ISBN

978-0-7695-3884-6

Type

conf

DOI

10.1109/ICETET.2009.37

Filename

5394995