Li Xin, Li Mingyang, Yu Moduo, Fan Qinqin
Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China.
Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China.
Biomimetics (Basel). 2023 Sep 18;8(5):431. doi: 10.3390/biomimetics8050431.
Distribution network reconfiguration involves altering the topology structure of distribution networks by adjusting the switch states, which plays an important role in the smart grid since it can effectively isolate faults, reduce the power loss, and improve the system stability. However, the fault reconfiguration of the distribution network is often regarded as a single-objective or multi-objective optimization problem, and its multimodality is often ignored in existing studies. Therefore, the obtained solutions may be unsuitable or infeasible when the environment changes. To improve the availability and robustness of the solutions, an improved discrete multimodal multi-objective particle swarm optimization (IDMMPSO) algorithm is proposed to solve the fault reconfiguration problem of the distribution network. To demonstrate the performance of the proposed IDMMPSO algorithm, the IEEE33-bus distribution system is used in the experiment. Moreover, the proposed algorithm is compared with other competitors. Experimental results show that the proposed algorithm can provide different equivalent solutions for decision-makers in solving the fault reconfiguration problem of the distribution network.
配电网重构涉及通过调整开关状态来改变配电网的拓扑结构，这在智能电网中起着重要作用，因为它可以有效地隔离故障、降低功率损耗并提高系统稳定性。然而，配电网的故障重构通常被视为单目标或多目标优化问题，并且现有研究中常常忽略其多模态性。因此，当环境变化时，所获得的解决方案可能不合适或不可行。为了提高解决方案的可用性和鲁棒性，提出了一种改进的离散多模态多目标粒子群优化（IDMMPSO）算法来解决配电网的故障重构问题。为了验证所提出的IDMMPSO算法的性能，实验中使用了IEEE33节点配电系统。此外，将所提出的算法与其他竞争算法进行了比较。实验结果表明，所提出的算法在解决配电网故障重构问题时可以为决策者提供不同的等效解决方案。
