A Data Imputation Model in Phasor Measurement Units Based on Bagged Averaging of Multiple Linear Regression

Abstract

Synchrophasor data from the phasor measurement unit (PMU) indicate the health of the electrical system. However, the PMU performance relies entirely on a supported communication network. The existing communication approaches do not guarantee the error-free channel for the PMU network. Meanwhile, the mean or median approaches are commonly used to impute the missing value. These methods, however, fail to recover some valuable frequency events. In this paper, we first proved the multiple linear regression features in many synchronized frequency data streams in the short-time window. Then, we proposed the Bagged Averaging of Multiple Linear Regression model, which handles and fulfills the missing values in synchronized frequency data measurement fast and efficiently. This technique was based on the ensemble learning by bootstrapping and averaging many multiple linear regressions to predict the missing values. Various experiments on the synchronized frequency measurement data from the Texas synchrophasor network have demonstrated the effectiveness of our proposed approach in recovering the missing frequency events. Our proposed approach guarantees the performance of real-time wide area monitoring system applications, such as frequency analysis or stability monitoring and trending.

Keywords

• Phasor measurement unit
• synchronized frequency data
• bagged averaging
• correlation analysis
• multiple linear regression
• bootstrap