Abstract
Power Quality (PQ) disturbances cause rigorous issues in classical and smart grids and industries. The identification of PQ disturbances and effective prevention of such events are essential. In this framework, this paper suggests an original real-time approach, based on event-driven processing, for time-domain PQ signals features extraction and classification. The idea is based on smartly combining the event-driven signal acquisition and segmentation along with local features extraction and voting based classification for realizing an efficient and high precision solution. The classification algorithm is described. The system functionality is tested for a case study, and results are presented. The first order of magnitude reduction in the accumulated count of samples is achieved by the devised approach as compared to the traditional counterparts. It confirms a significant processing gain and effectiveness in terms of power consumption of the suggested system compared to the peers. The proposed system attains an average recognition accuracy of 99%, for the case of three-class PQ signals. It demonstrates the benefits of using the suggested solution for the realization of computationally efficient automatic PQ disturbances elucidators. (C) 2020 The Authors. Published by Elsevier B.V.