Wavelet Transform-Spectral Kurtosis Based Hybrid Technique for Disturbance Detection in a Microgrid

This paper proposes a combined Wavelet Transform-Spectral Kurtosis based approach for detecting islanding and power quality (PQ) issues in microgrids. Islanding and PQ disturbances are generated in a microgrid comprising renewable energy sources such as wind and solar photovoltaic apart from diesel generators, fuel cells and flywheel/battery energy storage systems (FESS/BESS). Different microgrid configurations are considered to test the detection capabilities of the proposed approach. The negative sequence component of the voltage signal is measured at the point of common coupling (PCC) and processed through Short Time Fourier Transform (STFT), Wavelet Transform (WT) and Wavelet Transform-Spectral Kurtosis (SK) under no-noise and 20-dB noise conditions. Furthermore, performance indices such as energy and kurtosis are calculated in all case studies to detect disturbances based on a suitably selected threshold. The results of the case studies demonstrate the superior performance and robustness of SK when compared with STFT and WT for detecting islanding and PQ disturbances in MGs.