Early anomaly detection and localisation in distribution network: A data-driven approach

Abstract

The measurement data collected from the supervisory control and data acquisition (SCADA) system installed in distribution network can reflect the operational state of the network effectively. In this study, a random matrix theory-based approach is developed for early anomaly detection and localisation by using the data. For every feeder in the distribution network, a corresponding data matrix is formed. Based on the Marchenko-Pastur law for the empirical spectral analysis of covariance 'signal+noise' matrix, the linear eigenvalue statistics are introduced to indicate the anomaly, and the outliers and their corresponding eigenvectors are analysed for locating the anomaly. As for the low observability feeders in the distribution network, an increasing data dimension algorithm is designed for the formulated low-dimensional matrices being more accurately analysed. The developed approach can detect and localise the anomaly at an early stage, and it is robust to random disturbance and measurement error. Cases on Matpower simulation data and real SCADA data corroborate the feasibility of the approach.