An Innovative Single Shot Power Quality Disturbance Detector Algorithm

Power quality disturbances (PQDs) have affected many people due to the growing number of electronic nonlinear loads and because of the significant increase of renewable sources connected to the grid. Previous works have shown the development of algorithms to detect and classify these disturbances. A thorough review of PQD detector algorithms pointed out the use of machine learning and deep learning algorithms as the most used, accurate and up-to-date approaches to deal with this problem. Up until now, these algorithms were used in a sliding window manner that often fail to identify more than one disturbance in a single window frame. In this work, an innovative architecture called single shot PQD detection (SSPQDD) has been developed to solve this problem. Several experiments were conducted using a simulation dataset to validate the performances of the proposed SSPQDD in comparison with other algorithms available in literature in terms of computational resources, accuracy of identification, and number of layers. Furthermore, an experimental testbench has been carried out to test the performances of SSPQDD using real measurement data in case of multiple disturbances in a single window frame. The overall accuracy obtained using the proposed SSPQDD was 96.55% in PQD detection.

Automated summary

Power quality disturbances have become a concern for many due to the increasing number of nonlinear loads and renewable sources connected to the grid. This work presents a novel algorithm, SSPQDD, which outperforms other algorithms in terms of computational resources, accuracy, and layers. Experimental results using simulation and real measurement data validate the effectiveness of SSPQDD in detecting PQDs with an overall accuracy of 96.55%.