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Power quality disturbances (PQDs) have major challenges in embedded generation systems, renewable energy networks, and HVDC/HVAC electrical power transmission networks. Due to PQDs, electrical power network can have disruption in the protection system, security system, and energy-saving system. PQDs also affect the operation cost and consistency of electrical power systems. This paper presents an innovative method based on compressive sensing (CS), singular spectrum analysis (SSA), wavelet transform (WT) and deep neural network (DNN) for monitoring and classification of PQDs. Feature extraction and selection is an essential part of the classification of PQDs. In this paper, initially, SSA time-series tool and multi-resolution wavelet transform are introduced to extract the features of PQDs, and then CS technique is used to reduce the dimensionality of the extracted features. Finally, DNN-based …