Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Abstract

The classification of single and simultaneous power quality disturbances (PQDs) has become an issue of concern in the power system field. This paper proposes a novel approach based on dual strong tracking filters (STFs) and the rule-based extreme learning machine (ELM) for detecting and classifying single and simultaneous PQDs. Dual STFs are a hybrid structure of a low-order STF and high-order STF. The fading factor of the low-order STF is used to detect sudden changes in PQDs; the fundamental amplitude variation is tracked by the high-order STF. Six distinctive features extracted from the dual STFs serve as the input to the ELM classifier for PQD classification. The rule-based ELM technique, which is equipped with certain decision rules, can improve the ELM classification accuracy when the number of hidden nodes is insufficient. In consideration of special structures of matrices, the real-time computation of the proposed method can be realized. A PQD dataset is generated in MATLAB for simulation experiments; the results show that 20 types of PQDs, including single and simultaneous disturbances, can be accurately classified under the different levels of noise via the proposed method. The method is also tested on a real recorded waveform to verify its effectiveness in PQD classification.