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<p>Traditional power system state estimation (PSSE) methods face challenges when dealing with increasingly complex grid dynamics and non-Gaussian noise. Data-driven methods offer new insights into addressing these issues, but capacities in capturing complex spatio-temporal correlations can still be enhanced. Therefore, based on the Spatio-Temporal Transformer (STT) and long short-term memory (LSTM), a novel and robust PSSE method named as STTL using the complementary and parallel feature extraction architecture is proposed for power systems. The Spatio-Temporal Transformer employing a decoupled spatio-temporal attention mechanism is designed to capture long-term temporal dependencies and global spatial correlations across the system, thereby effectively learning the operational patterns and electrical coupling of the power system. Meanwhile, the LSTM based component is developed to focus on modeling local sequence dynamics in the evolution of time series data. Simulation results from IEEE 14-bus, 57-bus, and 118-bus systems demonstrate that the proposed STTL method achieves high estimation accuracy and computational efficiency in both Gaussian and non-Gaussian noise scenarios.</p>

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A Robust Power System State Estimation Method Using Complementary Spatio-Temporal Feature Extraction

  • 期刊论文

  • 发表日期：

    2026年7月13日

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  • Tengpeng Chen,
  • Weize Jing,
  • Chen Zhang,
  • Eddy Y. S. Foo,
  • Nianyin Zeng,
  • Po Li

摘要

Traditional power system state estimation (PSSE) methods face challenges when dealing with increasingly complex grid dynamics and non-Gaussian noise. Data-driven methods offer new insights into addressing these issues, but capacities in capturing complex spatio-temporal correlations can still be enhanced. Therefore, based on the Spatio-Temporal Transformer (STT) and long short-term memory (LSTM), a novel and robust PSSE method named as STTL using the complementary and parallel feature extraction architecture is proposed for power systems. The Spatio-Temporal Transformer employing a decoupled spatio-temporal attention mechanism is designed to capture long-term temporal dependencies and global spatial correlations across the system, thereby effectively learning the operational patterns and electrical coupling of the power system. Meanwhile, the LSTM based component is developed to focus on modeling local sequence dynamics in the evolution of time series data. Simulation results from IEEE 14-bus, 57-bus, and 118-bus systems demonstrate that the proposed STTL method achieves high estimation accuracy and computational efficiency in both Gaussian and non-Gaussian noise scenarios.