Abstract
In the present work, a novel technique for detection and classification of power quality disturbance events (PQDE) in noisy environment is proposed employing adaptive superlet transform (ALST)-based time-frequency analysis and deep learning technique. ALST is a powerful signal processing tool for analysis of non-stationary signals in time-frequency frame. To this end, synthetic PQDEs were initially generated following IEEE std. 1159–2009. The generated 1D PQDEs were transformed to 2-D time-frequency RGB images using ASLT. The transformed time-frequency images of 1D PQDEs employing ALST showed enhanced resolution in time-frequency frame. In addition, ALST analysis of PQDEs showed distinct representations in time-frequency plane even in presence of very high noise level. The obtained PQDEs obtained using ASLT were finally fed as inputs to a designed lightweight customized convolutional neural network (CNN) architecture for automated feature extraction and classification. In addition, the performance of the proposed model was further evaluated using benchmark CNN models. It has been found that the proposed approach is highly accurate and has returned better performance compared to other time-frequency representation and existing approaches. The results showed that the proposed framework is capable of diagnosis of power quality disturbance events in both noise-free and strong noisy environment. In addition, the proposed CNN model is light weight and fully customized and required less computational time and memory compared to existing CNN models.