A Synchrophasor Data Compression Technique With Iteration-Enhanced Phasor Principal Component Analysis

The phasor data were compressed as separated amplitudes and phases in previous synchrophasor data compression techniques. To utilize the spatial correlation and temporal continuity of synchrophasors for data compression, a phasor principal component analysis (PPCA) in the field of complex numbers is proposed to compress synchrophasors as a whole in this article. Then, an iterative phasor principal components selection method is proposed to achieve PPCA and ensure the accuracy of reconstructed data since the existing eigenvalue-based criteria are not suitable for data compressions. Moreover, the proposed PPCA is enhanced by an iteration-based process to reduce the computation of PPCA. Actual PMU data measured under both a low-frequency oscillation incident and a two-phase short circuit incident conditions are used to verify the performance of PPCA compared with a recent PCA-based compression method. The results demonstrate that PPCA achieves higher compression ratios with better accuracy of reconstructed data, significantly reduced computation, and better real-time performance under both conditions.

I. Introduction

Phasor Measurement Units (PMU) and Wide-area Measurement Systems (WAMS) provide synchronized phasor measurements with considerably higher reporting rates. The massive amount of PMU measurement data is a severe factor that limits further applications of synchronous measurements. Since most of the PMU data are phasor data, also known as synchrophasors, the compression of phasor data will directly affect the compression ratio and the accuracy of reconstructed data, which are two leading indicators for the performance of data compression. Therefore, synchrophasor data compression techniques are needed to achieve higher efficiency of PMU data storage and communication [1]–[8].