于艾清, 丁丽青, 王育飞, 李豪. 基于故障邻接状态的配电网多故障抢修与优化策略[J]. 中国电力, 2023, 56(3): 64-76. DOI: 10.11930/j.issn.1004-9649.202103145
引用本文: 于艾清, 丁丽青, 王育飞, 李豪. 基于故障邻接状态的配电网多故障抢修与优化策略[J]. 中国电力, 2023, 56(3): 64-76. DOI: 10.11930/j.issn.1004-9649.202103145
YU Aiqing, DING Liqing, WANG Yufei, LI Hao. Multi-fault Repair and Optimization Strategy of Distribution Network Based on Fault Adjacency State[J]. Electric Power, 2023, 56(3): 64-76. DOI: 10.11930/j.issn.1004-9649.202103145
Citation: YU Aiqing, DING Liqing, WANG Yufei, LI Hao. Multi-fault Repair and Optimization Strategy of Distribution Network Based on Fault Adjacency State[J]. Electric Power, 2023, 56(3): 64-76. DOI: 10.11930/j.issn.1004-9649.202103145

基于故障邻接状态的配电网多故障抢修与优化策略

Multi-fault Repair and Optimization Strategy of Distribution Network Based on Fault Adjacency State

  • 摘要: 基于故障与配网的拓扑关系和抢修与恢复的动态交替特性,建立了基于故障邻接状态的配电网多故障抢修与优化模型,快速制定抢修策略。在故障抢修阶段,基于供电类型建立负荷节点带电状态矩阵,提取故障邻接负荷的带电状态建立故障邻接负荷带电状态矩阵,对其进行拓展得到故障邻接状态,并对故障进行分类。通过抢修与故障邻接状态的交替更新确定每阶段最优抢修任务。在重构计算中,建立自适应环压有序环矩阵作为算法的解空间,引入余弦递减函数和莱维飞行对量子粒子群算法进行改进,建立莱维系数量子粒子群算法进行求解。用PG&E69节点系统进行仿真,验证所提方法的可行性和所提算法的有效性。

     

    Abstract: According to the relationship between faults and topology of the distribution network as well as the coupling between repair and recovery, a multi-fault repair and optimization model of distribution network based on fault adjacency state is established in this paper. In the stage of emergency repair, the electrified state matrix of load nodes is established based on the power supply type. The electrified state matrix of the fault adjacent load is proposed by extracting the electrified state of the fault adjacent load. The fault adjacency state is obtained by extending the electrified state matrix of the fault adjacent load, then the faults are classified. The optimal repair task at each stage is determined by the alternate update of repair and fault adjacent state. During the reconstruction calculation period, the node voltage based adaptive ordered ring matrix for the ring network is set up as the solution space of the algorithm. Then, the Levy coefficient quantum particle swarm optimization was applied using the decreasing cosine function and Levy flight to improve the quantum particle swarm optimization algorithm. The practicability and the effectiveness of the proposed methods are verified by simulation on PG&E69 bus system.

     

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