在分布网络中的故障重构基于改进的离散多模式多目标粒子群优化算法.
Xin Li1, Mingyang Li1, Moduo Yu2
1Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China.
Biomimetics (Basel, Switzerland)
|September 27, 2023
概括
本研究介绍了一种改进的离散多式多目标粒子群集优化 (IDMMPSO) 算法,用于分配网络重新配置. IDMMPSO算法通过解决故障场景中被忽视的多模式性来提高智能电网的解决方案可用性和稳定性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 优化理论 优化理论
背景情况:
- 配电网络的重新配置对于智能电网至关重要,它会影响故障隔离,减少功耗损失和系统稳定性.
- 现有的故障重新配置优化方法往往忽视多模式性,导致潜在的不合适或不可行的解决方案.
- 电力系统的动态性需要强大的重新配置策略,以适应不断变化的环境条件.
研究的目的:
- 提出一个改进的离散多模式多目标粒子群集优化 (IDMMPSO) 算法.
- 通过将多模式纳入故障重配置优化中,解决现有方法的局限性.
- 提高配电网络故障重配置解决方案的可用性和稳定性.
主要方法:
- 开发了改进的离散多模式多目标粒子群集优化 (IDMMPSO) 算法.
- 将IDMMPSO算法应用于IEEE33总线分布系统进行故障重新配置.
- 对拟议的IDMMPSO算法与竞争算法的比较分析.
主要成果:
- IDMMPSO算法有效地解决了分布网络中的故障重新配置问题.
- 在IEEE33总线系统上的实验结果证明了算法的性能.
- 拟议的算法为决策者提供了多样化和同等的解决方案.
结论:
- 通过考虑多模式,IDMMPSO算法为分配网络故障重新配置提供了一种优越的方法.
- 增强的算法提高了智能电网运营的可靠性和适应性.
- 这项研究为优化故障条件下的配电网络管理提供了有价值的工具.
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