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A prediction-aided state estimation (FASE) method is studied to realize real-time state perception of the power system. The process is based on the phase measurement unit (PMU) measurement data. Firstly, a linear state estimation model based on PMU is constructed. Secondly, an accurate system state prediction value is obtained based on a spatiotemporal multi-view learning algorithm. Finally, the state-predicted value filters the latest measured value to assist the state estimation process. The simulation outcomes demonstrate that this approach achieves a precise and efficient real-time online state estimation for power systems.

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A Prediction-Aided State Estimation Method Based on PMU Measurement

  • 会议论文

  • 发表日期：

    2025年

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  • Zhida Lin,
  • Xiyuan Ma,
  • Zhuohuan Li,
  • Sirui Yang,
  • Yansen Chen

摘要

A prediction-aided state estimation (FASE) method is studied to realize real-time state perception of the power system. The process is based on the phase measurement unit (PMU) measurement data. Firstly, a linear state estimation model based on PMU is constructed. Secondly, an accurate system state prediction value is obtained based on a spatiotemporal multi-view learning algorithm. Finally, the state-predicted value filters the latest measured value to assist the state estimation process. The simulation outcomes demonstrate that this approach achieves a precise and efficient real-time online state estimation for power systems.