Classification of Power Quality Events Using Optimal Time-Frequency Representations—Part 1: Theory

Abstract

Better software and hardware for automatic classification of power quality (PQ) disturbances are desired for both utilities and commercial customers. Existing automatic recognition methods need improvement in terms of their capability, reliability, and accuracy. This paper presents the theoretical foundation of a new method for classifying voltage and current waveform events that are related to a variety of PQ problems. The method is composed of two sequential processes: feature extraction and classification. The proposed feature extraction tool, time-frequency ambiguity plane with kernel techniques, is new to the power engineering field. The essence of the feature exaction is to project a PQ signal onto a low-dimension time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes. The technique of designing an optimized TFR from time-frequency ambiguity plane is for the first time applied to the PQ classification problem. A distinct TFR is designed for each class. The classifiers include a Heaviside-function linear classifier and neural networks with feedforward structures. The flexibility of this method allows classification of a very broad range of power quality events. The performance validation and hardware implementation of the proposed method are presented in the second part of this two-paper series.