An improved fuzzy C-Means algorithm for power load characteristics classification

A simulated annealing and genetic algorithm oriented Fuzzy C-Means (SAGA-FCM) algorithm is used for load classification to improve the accuracy and validity. The traditional Fuzzy C-Means (FCM) algorithm is sensitive to its initial cluster centers, and it is easy to fall into the local optimum. Whil...

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Bibliographic Details
Published in:Dianli Xitong Baohu yu Kongzhi Vol. 40; no. 22; pp. 58 - 63
Main Authors: Zhou, Kai-Le, Yang, Shan-Lin
Format: Journal Article
Language:Chinese
Published: 16.11.2012
Subjects:
Algorithms
Classification
Fuzzy
Fuzzy logic
Fuzzy set theory
Genetic algorithms
Searching
Simulated annealing
ISSN:1674-3415
Online Access:Get full text
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Summary:A simulated annealing and genetic algorithm oriented Fuzzy C-Means (SAGA-FCM) algorithm is used for load classification to improve the accuracy and validity. The traditional Fuzzy C-Means (FCM) algorithm is sensitive to its initial cluster centers, and it is easy to fall into the local optimum. While SAGA-FCM algorithm integrates the strong local search ability of simulated annealing algorithm and the strong global search ability of genetic algorithm to overcome the drawbacks of traditional FCM algorithm. Meanwhile, the hierarchical clustering method, K-Means algorithm and traditional FCM algorithm are also used for power load classification. The comparative analysis from the experimental results shows that SAGA-FCM algorithm is more effective and superior than the other three algorithms.
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ISSN:1674-3415