loading page

Pseudo-Measurement based State Estimation for Railway Power Supply Systems with Renewable Energy Resources
  • Zheng Pan,
  • Liang Che,
  • Chunming Tu
Zheng Pan
Hunan University
Author Profile
Liang Che
Hunan University

Corresponding Author:[email protected]

Author Profile
Chunming Tu
Hunan University College of Electrical and Information Engineering
Author Profile


State estimation is critical for railway power supply systems (RPSSs). Pseudo-measurement is commonly used in state estimation. However, the fluctuations of renewable generations and railway traction loads in RPSS may introduce data noise, which will jeopardize the accuracy of the generated pseudo-measurements and thus impact the state estimation. Additionally, when learning the historical measurement data sequences, the traditional pseudo-measurement model is likely to have overfitting, which will further impact the accuracy of pseudo-measurements, thereby affecting the accuracy of state estimation. To address these issues, this paper proposes a high-accuracy pseudo-measurement-based state estimation approach for RPSSs. Firstly, a denoising autoencoder (DAE)-based method is used to mitigate the impact of data noise on the accuracy of pseudo measurements, and a gated recurrent unit (GRU)-based method is used to adaptively learn the historical measurement data sequence, thereby improving the accuracy of pseudo measurements. Next, the pseudo-measurement weights are obtained by generating pseudo-measurement variances using the Gaussian mixture model. Finally, the pseudo measurements and real-time measurements are integrated by weighted least squares to realize the state estimation of RPSS. The effectiveness and accuracy of the proposed method are verified by simulation on a modified IEEE 33-node system which includes a railway traction substation and renewable generations.
28 Jun 2023Submitted to IET Generation, Transmission & Distribution
05 Jul 2023Assigned to Editor
05 Jul 2023Submission Checks Completed
05 Sep 2023Reviewer(s) Assigned
05 Nov 2023Review(s) Completed, Editorial Evaluation Pending
06 Nov 2023Editorial Decision: Revise Major