Multi-Rate PMU Data Fusion in Power Systems via Low Rank Tensor Train

With the continuous development of power systems, WAMS have become increasingly important for real-time system monitoring. As the core devices of WAMS, PMUs can provide synchronized, high-precision, and high-resolution measurements of power system states. However, in practical applications, PMUs deployed in different regions often operate at different sampling rates, resulting in multi-rate measurement data and posing challenges for data fusion. To address this issue, this paper proposes a multi-rate PMU data fusion method based on low-rank TT. Specifically, the proposed method first performs tensor-based modeling of multi-rate measurement data, embedding multidimensional correlations into a high-order tensor representation. Then, a data completion model is constructed through low-rank TT decomposition to effectively capture cross-timescale dependencies. Finally, an efficient numerical solution is developed to expand low-resolution measurements into high-resolution data, thereby achieving unified data fusion. Case studies on both simulated and real-world PMU measurement data demonstrate that the proposed approach outperforms traditional interpolation and matrix completion methods, achieving superior reconstruction accuracy and robustness.