Identification of Major Power Quality Disturbance Sources in Regional Grid based on Monitoring Data Correlation Analysis

This paper proposes a method of identifying major power quality disturbance sources based on monitoring data correlation analysis among multiple grid nodes in a regional power grid. First, using correlation calculation between the voltage quality index in each node and the current quality index in each branch, strong-correlated branches which significantly affect voltage distortion in problematic nodes are extracted. Second, the contribution of each strong-correlated current branch to node voltage distortion is quantified through partial correlation analysis. Finally, the major disturbance sources are identified by means of contribution estimation. The proposed method is validated through a case study conducted on a regional power grid which includes multiple types of disturbance sources. The results evidenced that the method can identify correctly the major disturbance sources responsible for node voltage distortion, thus providing effective guidance for disturbance source governance.

I. Introduction

WITH the rapid growth of nonlinear and fluctuating loads such as electrified railways, wind farms and photovoltaic stations, Power Quality (PQ) has become a critical issue in recent years. Due to complexity of power grid, the PQ level of each power grid node is strongly associated with that of many others. Therefore, identification of major disturbance sources, i.e. sources contribute most to PQ distortion of problematic nodes, has attracted much research interest [1].