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Power quality monitors handle and store several gigabytes of data within a week and hence automatic detection, recognition and analysis of power disturbances require robust data mining techniques. Literature reveals that much work has been done to evolve several feature extraction and subsequent classification techniques for accurate power disturbance pattern recognition. However, the features extracted have been rarely evaluated for their usefulness. The objective of this paper is to emphasize that feature selection is an important issue in power quality disturbance classification and that genetic algorithms can select good subsets of features. In this paper, a wrapper based approach that integrates multi-objective genetic algorithms and the target learning algorithm is presented in order to evolve optimal subsets of discriminatory features for robust pattern classification.
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