TY - GEN
T1 - A Novel State Estimation Method for Modern Power Systems Based on Multi-Source Data Cleaning
AU - Mou, Shanke
AU - Yang, Nan
AU - Chen, Hao
AU - Huang, Wei
AU - Zhu, Xuanwen
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper proposes a state estimation method based on multi-source data cleaning and fusion to address issues of poor data quality, low estimation accuracy and low efficiency in the measurement of renewable energy distribution grids. First, a method is proposed for identifying and correcting poor data using a Temporal Convolutional Network (TCN) and a Bidirectional Long Short-Term Memory Network (BILSTM), to clean real-time, multi-source measurement data. Secondly, a hybrid linear state estimation method considering renewable energy grid connection is employed to reflect the real-time state of the distribution grid. Simulation results demonstrate that the proposed data cleansing method exhibits high identification rates and correction accuracy, while the proposed state estimation method has high accuracy and real-time performance. This provides a solid foundation for the management and operation of distribution grids and smart power systems.
AB - This paper proposes a state estimation method based on multi-source data cleaning and fusion to address issues of poor data quality, low estimation accuracy and low efficiency in the measurement of renewable energy distribution grids. First, a method is proposed for identifying and correcting poor data using a Temporal Convolutional Network (TCN) and a Bidirectional Long Short-Term Memory Network (BILSTM), to clean real-time, multi-source measurement data. Secondly, a hybrid linear state estimation method considering renewable energy grid connection is employed to reflect the real-time state of the distribution grid. Simulation results demonstrate that the proposed data cleansing method exhibits high identification rates and correction accuracy, while the proposed state estimation method has high accuracy and real-time performance. This provides a solid foundation for the management and operation of distribution grids and smart power systems.
KW - data cleaning
KW - deep neural networks
KW - multi-source measurement data
KW - state estimation
UR - https://www.scopus.com/pages/publications/105037326782
U2 - 10.1109/EPSIC68233.2025.00061
DO - 10.1109/EPSIC68233.2025.00061
M3 - Conference contribution
AN - SCOPUS:105037326782
T3 - Proceedings - 2025 2nd International Conference on Electrical Power Systems and Intelligent Control, EPSIC 2025
SP - 308
EP - 312
BT - Proceedings - 2025 2nd International Conference on Electrical Power Systems and Intelligent Control, EPSIC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Electrical Power Systems and Intelligent Control, EPSIC 2025
Y2 - 15 August 2025 through 17 August 2025
ER -