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A Diagnosis Method of Power Flow Convergence Failure for Bulk Power Systems Based on Intermediate Iteration Data

Author

Listed:
  • Gang Mu

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin City 132012, China)

  • Yibo Zhou

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin City 132012, China)

  • Mao Yang

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin City 132012, China)

  • Jiahao Chen

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin City 132012, China)

Abstract

Power flow calculation is the foundation of security analyses in a power system, and the phenomenon of convergence failure is becoming more prominent with the expansion of the power grid. The existing convergence failure diagnosis methods based on optimization modeling and local feature recognition are no longer viable for bulk power systems. This paper proposes a diagnosis method based on intermediate iteration data and the identification of the transmission power congested channel. Firstly, the transmission power congestion index is constructed, and then a method for identifying transmission congestion channels is proposed. The reasons for convergence failure of the power flow are diagnosed from two aspects: excessive power to be transmitted and insufficient transmission capacity. Finally, with the aim of alleviating transmission channel congestion, a correction strategy for power flow injection space data was constructed, which generates relaxation schemes for operational variables. The effectiveness of the proposed strategy was verified using the simulation results of an actual provincial power grid and a standard example power system with 13,659 buses. The method proposed in this paper is entirely based on intermediate power flow iteration data, which avoids the complex modeling of the power flow adjustment and provides methodological support for power flow diagnosis in bulk power systems.

Suggested Citation

  • Gang Mu & Yibo Zhou & Mao Yang & Jiahao Chen, 2023. "A Diagnosis Method of Power Flow Convergence Failure for Bulk Power Systems Based on Intermediate Iteration Data," Energies, MDPI, vol. 16(8), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3540-:d:1127565
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    References listed on IDEAS

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    1. Steinegger, Josef & Wallner, Stefan & Greiml, Matthias & Kienberger, Thomas, 2023. "A new quasi-dynamic load flow calculation for district heating networks," Energy, Elsevier, vol. 266(C).
    2. Alex Valenzuela & Silvio Simani & Esteban Inga, 2021. "Automatic Overcurrent Protection Coordination after Distribution Network Reconfiguration Based on Peer-To-Peer Communication," Energies, MDPI, vol. 14(11), pages 1-22, June.
    3. Jingcheng Zhou & Wei Wei & Ruizhi Zhang & Zhiming Zheng, 2021. "Damped Newton Stochastic Gradient Descent Method for Neural Networks Training," Mathematics, MDPI, vol. 9(13), pages 1-12, June.
    4. Xiao, Hao & Pei, Wei & Wu, Lei & Ma, Li & Ma, Tengfei & Hua, Weiqi, 2023. "A novel deep learning based probabilistic power flow method for Multi-Microgrids distribution system with incomplete network information," Applied Energy, Elsevier, vol. 335(C).
    5. Edy Quintana & Esteban Inga, 2022. "Optimal Reconfiguration of Electrical Distribution System Using Heuristic Methods with Geopositioning Constraints," Energies, MDPI, vol. 15(15), pages 1-20, July.
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