Multi-level Signal Decomposition for Power Quality Disturbance Classification

Abstract

The introduction of electric vehicles impose large disturbance to the grid-level power signal due to the charging and discharging mechanism. Power signal monitoring in the electrical grid can provide several insights such as power quality disturbance detection, major power consumption area, peak power usage period, and their potential catastrophic failure conditions. As for preventive maintenance purpose, automatic classification of power quality disturbance using a hybrid method incorporating wavelet transform and deep LSTM network is proposed in this paper. Multi-level signal decomposition is applied to input signal to increase the resolution of input decomposing into multiple frequency bands. Subsequently, these multi-level frequency components are fed into deep LSTM layer to further extract useful higher order latent feature. Classification performance of the proposed wavelet-based LSTM (WTLSTM) network is bench-marked with deep LSTM method. Additive white Gaussian noise (AWGN) with signal-to-noise (SNR) levels between 20-50dB are inserted during the training process to increase the generalization of signal learning with the realistic scenarios. The classification performance of both WT-LSTM and Deep LSTM networks are tested with 20,30,40,50dB SNR AWGN and noiseless conditions. As a result, the WT-LSTM network obtains an overall classification performance of 89.77% on 20dB and 99.21% on noiseless condition as compared to Deep LSTM, with 88.48% and 98.54% respectively.