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. Classification fusion combines multiple classifications of data into a single classification solution of greater accuracy. Feature extraction aims to reduce the computational cost of feature measurement, increase classifier efficiency, and allow greater classification accuracy based on the process of deriving new features from the original features.
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