Data compression techniques for Phasor Measurement Unit (PMU) applications in smart transmission grid

Abstract

The advances in Wide Area Measurement Systems (WAMS) and deployment of a huge number of phasor measurement units (PMUs) in the grid are generating big data volume. This data can be used for a variety of applications related to grid monitoring, management, operation, protection, and control. With the increase in this data size, the respective storage capacity needs to be enhanced. Also, communication infrastructure readiness remains bottleneck to transfer this big data. One of the probable solutions could be transmitting compressed data. This paper presents techniques for data compression in the smart transmission system using singular values decomposition (SVD) and the eigenvalues decomposition (EVD). The SVD and EVD based principal component analysis (PCA) techniques are applied to the real-time PMU data collected from extra-high voltage (EHV) substations of transmission utility in the western regional grid of India. Adequacy of data is checked by Kaiser-Meyer-Olkin (KMO) test in order to have the satisfactory performance of these techniques towards achieving the objective of efficient data compression. Results are found satisfactory gives compression more than 80% using real time data.

Synergizing PMU Data From Multiple Locations in Indian Power Grid-Case Study

“…This transmission system is capable to handle about 21000 MW of power. MSETCL comprises intra and interregional power transmission lines [21]. Important features of the MSETCL are given in Table I.…”

“…That might be due to the saturation of the current transformer and/or burden of the connecting cables. In [21], the data compression is performed on PMU data received from eight important substations of the power grid. It accommodates two major events in the Maharashtra State Electricity Transmission Company Limited (MSETCL) transmission network.…”