Data Analytics Based Power Quality Investigations in Emerging Electric Power System Using Sparse Decomposition

Abstract

Modern electric grid usages extensive non-linear loads leading to the increased harmonic current injection in the system. This paper proposes a data-driven algorithm based on a sparse decomposition approach with the Overcomplete Hybrid Dictionary (OHD) and provides in-depth mathematical analysis to detect and mitigate harmonics in emerging electric power systems. The sparse decomposition method is widely used in image processing applications with significant advantages to big-data applications. However, its application is not properly addressed in electric grids’ applications despite massive data generation in a smart grid environment. Hence, in this paper, the performance evaluation of a sparse decomposition-based algorithm using a greedy approach is proposed and carried out in a real-time simulation environment for Distribution Static Compensator (DSTATCOM) application for the real-time detection, classification, and mitigation of PQ events to show its suitability and effectiveness. Finally, the experimental results on a small-scale laboratory setup are presented to validate the effectiveness of the proposed sparse-based control algorithm for DSTATCOM in real-time applications. The comparative result shows that the proposed sparse-based method is advantageous for off-line PQ signal processing and capable of real-time detection, classification, and control of the power apparatus for harmonics mitigation.