Massive Streaming PMU Data Modeling and Analytics in Smart Grid State Evaluation Based on Multiple H
Massive Streaming PMU Data Modeling and Analytics in Smart Grid State Evaluation Based on Multiple High-Dimensional Covariance Tests
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("n4g )to O(_n2g ) 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.
Efforts are in place to take synchrophasor technology to evaluate power states and develop reliable operational procedures to better understand and manage the power grids with wide-area visualization tools using PMU data. These power state evaluation methods can be generally organized into two categories: model-based estimators and data driven estimators. Model-based analysis is a kind of traditional method for offline analysis of state evaluation in power systems.
In this paper, by exploiting the changes in the covariance matrix of different sampling periods of the streaming PMU data, we develop a novel power state evaluation algorithm using the multiple high dimensional covariance matrix tests.
The key features of the proposed test statistic are as follows.
1) it can jointly reveal the relative magnitude, duration (or so-called clearing time) and location of a system event; 2) it specifies no parameter distribution of the PMU data, which implies a wide range of the practical applications; 3) it is a real time data driven method without requiring any knowledge of the system model or topology; 4) it is a flexible state evaluation indicator without specifying an explicit relationship between data dimension and sample size; 5) it provides effective computation due to principal component calculation and redundant computation elimination. 6) it implements the asymptotic properties of the high dimensional PMU data to enhance the robustness of the test statistic.