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State Estimation Algorithm of Power System Based on Preconditioned Conjugate Gradient Iteration
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Affiliation:

1.Hangzhou Xiaoshan Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311200, China;2.Xiaoshan Xinmei Complete Electric Manufacturing Branch of Hangzhou Electric Power Equipment Manufacturing Co., Ltd., Hangzhou 311200, China;3.College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Abstract:

With the continuous development of provincial-regional power grid integration and transmission-distribution network integration in China, the dimension of power system calculation is getting higher and higher. As a basic component of power system situation awareness, state estimation needs to ensure its real-time performance. Weighted least squares (WLS) method is the most widely used state estimation method in power systems. Therefore, according to the time-consuming characteristic when solving matrix multiplication and linear equations in the Newton iteration by WLS, this paper designs a state estimation algorithm of power system based on preconditioned conjugate gradient iteration with the idea of conjugate gradient method in Krylov subspace method. This method uses incomplete LU decomposition to preprocess the original linear equations, and adopts graphics processing unit (GPU) parallel acceleration technology to accelerate matrix multiplication, linear equation preprocessing, and conjugate gradient method iteration. The case analysis shows that the method in this paper has obvious acceleration effect, low memory and video memory requirement, and less iterations of the linear system of equations preprocessed by the incomplete LU decomposition method, which can meet the real-time requirements of large-scale power system state estimation.

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Get Citation
[1]LI Jianbin, WANG Pengcheng, FU Kan, et al. State Estimation Algorithm of Power System Based on Preconditioned Conjugate Gradient Iteration[J]. Automation of Electric Power Systems,2021,45(14):90-96. DOI:10.7500/AEPS20200802003
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History
  • Received:August 02,2020
  • Revised:January 01,2021
  • Adopted:
  • Online: July 21,2021
  • Published: