Massive Streaming PMU Data Modeling and Analytics in Smart Grid State Evaluation Based on Multiple High-Dimensional Covariance Tests

TL;DR

This paper introduces a nonparametric, high-dimensional covariance test-based algorithm for robust power state evaluation in large-scale smart grids using massive streaming PMU data, improving computational efficiency and accuracy.

Contribution

It develops a novel, flexible covariance matrix test method for power state evaluation that handles high-dimensional streaming data without specific distribution assumptions.

Findings

Effective in large-scale systems like IEEE 118-bus and Polish 2383-bus.

Reduces computational complexity significantly.

Accurately detects system events and their characteristics.

Abstract

The analogous deployment of phase measurement units (PMUs), the increase of data quantum and the deregulation of energy market, all call for the robust state evaluation in large scale power systems. Implementing model based estimators is impractical because of the complexity scale of solving the high dimension power flow equations. In this paper, we first represent massive streaming PMU data as big random matrix flow. By exploiting the variations in the covariance matrix of the massive streaming PMU data, a novel power state evaluation algorithm is then developed based on the multiple high dimensional covariance matrix tests. The proposed test statistic is flexible and nonparametric, which assumes no specific parameter distribution or dimension structure for the PMU data. Besides, it can jointly reveal the relative magnitude, duration and location of a system event. For the sake of practical application, we reduce the computation of the proposed test statistic from $O(\varepsilon n_g^4)$ to $O(\eta n_g^2)$ by principal component calculation and redundant computation elimination. The novel algorithm is numerically evaluated utilizing the IEEE 30-, 118-bus system, a Polish 2383-bus system, and a real 34-PMU system. The case studies illustrate and verify the superiority of proposed state evaluation indicator.