Phase space reconstruction based classification of power disturbances using support vector machines
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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This paper presents a novel self-adaptive wavelet neural network method for automatic recognition and classification of power quality disturbances. The types of disturbances include harmonic distortions, flickers, voltage sags, voltage swells, voltage interruptions, voltage notches, voltage impulses and voltage transients. The self-adaptive wavelet neural network model constructed consists of four layers: input layer, preprocessing layer, hidden layer and output layer. The preprocessing layer is also called wavelet layer whose function is to extract features of power quality disturbances for recognition and classification; the other three layers just constitute the feedforward neural network whose function is to recognize and classify the types of power quality disturbances. The self-adaptive wavelet neural network has a good anti-interference performance, and the test and evaluation results demonstrate that utilizing it power quality disturbances can be recognized and classified effectively, accurately and reliably.