  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 paper, a random matrix
theory (RMT) based approach is developed for early anomaly detection and
localization 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 analyzed 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 analyzed. The developed approach can detect and
localize 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.
