PMU Data Compression in Power Systems Using Adaptive Rank-Based Tensor Ring

Phasor measurement units (PMUs) are increasingly being deployed in power systems due to their high sampling rates and diverse data sampling types. However, this undoubtedly poses significant challenges to data centers in terms of data storage and transmission. This article proposes an adaptive rank-based tensor ring (TR) method for PMU data compression to address these issues. More specifically, we first extend the orders of the PMU measurement data to achieve high-order tensorization. Subsequently, based on using the alternating least-squares method to decompose the high-order data TR, we introduce a rank-increment strategy to obtain adaptive ranks. Using a TR data structure, the proposed method can transform high-order data with exponentially increasing volumes into a polynomial scale. This allows us to achieve cost-effective PMU data compression. The simulation results using real-world PMU measurement data reveal the excellent performance of our proposed method.