Two-Stage Dynamic Partitioning Strategy Based on Grid Structure Feature and Node Voltage Characteristics for Power Systems

Lixia Sun (), Xianxue Sha, Shuo Zhang, Jiahao Wang and Yiping Yu
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Lixia Sun: School of Electrical and Power Engineering, Hohai University, Nanjing 210098, China
Xianxue Sha: School of Electrical and Power Engineering, Hohai University, Nanjing 210098, China
Shuo Zhang: School of Electrical and Power Engineering, Hohai University, Nanjing 210098, China
Jiahao Wang: School of Electrical and Power Engineering, Hohai University, Nanjing 210098, China
Yiping Yu: School of Electrical and Power Engineering, Hohai University, Nanjing 210098, China

Energies, 2025, vol. 18, issue 10, 1-19

Abstract: To enhance the adaptability of grid partitioning under transient scenarios, this paper proposes a two-stage dynamic partitioning strategy based on structure–function coupling. Electrical coupling strength is first characterized using short-circuit impedance and the sensitivity between reactive power and voltage, while transient voltage correlation is incorporated through cosine similarity as edge weights in a graph model. Grid partitioning is then conducted by maximizing modularity through a staged approach that ensures network connectivity and automatically determines partition numbers. Case studies on the modified IEEE 39-bus system demonstrate that compared with transient voltage-based partitioning and conventional complex network methods, the proposed approach improves modularity by 69%, reduces the maximum post-fault voltage deviation by 38.6%, and achieves the highest regional decoupling rate. The result shows strong intra-regional cohesion and weak inter-regional connectivity, verifying the strategy’s effectiveness in enhancing adaptability and decoupling under transient conditions.

Keywords: structure–function coupling; electrical coupling strength; node voltage correlation; two-stage partitioning; modularity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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