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Regular Paper | Open Access

PMU-based Linear Power Flow Calculation Method with Minimizing Probability Errors

Wenchao Song1, Junjie Lin2, Jingbo He3, Chao Lu1( )
Department of Electrical Engineering, Tsinghua University, Beijing 100084 China
College of Electric Engineering and Automation, Fuzhou University, Fuzhou 350108 China
National Power Dispatching and Control Center, State Grid Corporation of China, Beijing 100031 China
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Abstract

With large-scale access to renewable energy, long-distance transmission via hybrid AC-DC networks and frequent load fluctuations, measurement-based online power flow calculation needs to be adapted to the volatility and randomness of modern power systems, and high accuracy and real-time performance are required for online power flow. However, the traditional power flow method suffers from lengthy computation, convergence difficulty and inaccuracy caused by RTU asynchronous errors. Therefore, a PMU-based online accurate linear power flow method that minimizes the probability of errors in power flow is proposed to improve accuracy and real-time performance. This power flow method no longer bounds PQ, PV or Vθ buses, and establishes a linear relationship between the voltage and current measurements. To address the problems of complicated PMU measurement errors and the high proportion of low-quality data, a 0–1 integer programming model is established to select a set of linear measurement equations for minimizing probability errors, and a complete method of PMU-based linear power flow calculation is proposed. Finally, the efficacy of the proposed linear power flow calculation method is verified on an IEEE 39 bus system and a large-scale power grid in China.

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CSEE Journal of Power and Energy Systems
Pages 1914-1925

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Cite this article:
Song W, Lin J, He J, et al. PMU-based Linear Power Flow Calculation Method with Minimizing Probability Errors. CSEE Journal of Power and Energy Systems, 2026, 12(4): 1914-1925. https://doi.org/10.17775/CSEEJPES.2023.06990

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Received: 28 August 2023
Revised: 22 December 2023
Accepted: 04 February 2024
Published: 21 February 2025
© 2023 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).