Classification of complex power quality disturbances based on modified empirical wavelet transform and light gradient boosting machine

Accurate detection and classification of power quality (PQ) disturbances is an essential prerequisite for PQ mitigation. To address this issue, a new PQ assessment framework based on modified empirical wavelet transform (MEWT) and light gradient boosting machine (LightGBM) is proposed here. First, the frequency estimation mechanism and band segmentation rule of empirical wavelet transform (EWT) is modified, which makes EWT suitable for analysing complex PQ signals. Second, based on the PQ disturbance analysis results of MEWT, 8 characteristic curves are defined and statistical features are extracted in both the time and frequency domain. Third, binary relevance-based LightGBM (BR-LightGBM) is designed for the multilabel classification of massive PQ events. Considering the impact of input features and model structure, feature selection and hyper parameter optimization are conducted for achieving better classification performance. Finally, extensive experiments based on synthetic data and two groups of measured data show the effectiveness of the proposed method on 48 types of complex PQ disturbances. Compared with other algorithms for multiple PQ signals, the proposed method is fast for computation and performs better in classification accuracy and robustness, which is a prospective alternative for the PQ monitoring system.