| 基于改进惯性权重粒子群算法的抢修小组快速调配策略 |
| 点此下载全文 |
| 引用本文：孟荣,赵冀宁,周通.基于改进惯性权重粒子群算法的抢修小组快速调配策略[J].电网与清洁能源,2021,37(7):17~24 |
| 摘要点击次数: 126 |
| 全文下载次数: 0 |
|
| 基金项目:国家自然科学基金（51877076） |
|
| 中文摘要:为减少电网抢修人员的调度时间以及提高电网的抢修效率，从前期保障角度建立了基于最短距离的抢修小组驻地选择模型；为解决后期多点故障发生后的调度问题，建立了基于最小维修时间的抢修小组调度模型。通过引入动态调整的惯性权重对粒子群算法（particle swarm optimization，PSO）进行改进，使算法的局部收敛性和全局收敛性均有所提高。同时针对旅行商（traveling salesman problem，TSP）问题，增强了算法解决高维度、顺序优化问题的能力。某城市50节点算例的仿真研究表明，所提方法能够准确有效地确定抢修小组驻地位置，当电网发生多处故障后能够迅速高效地确定应急抢修顺序方案、减小抢修工作调配时间，从而降低了因停电所造成的损失。 |
| 中文关键词:驻地选择 抢修顺序 惯性权重 TSP PSO |
| |
| Rapid Deployment Strategy of Emergency Repair Team |
|
|
| Abstract:In order to reduce the personnel dispatching time in the power grid emergency repair process and improve the efficiency of the power grid emergency repair， the shortest distance-based emergency repair team location selection model is established from the perspective of early support. To solve the scheduling problem after the occurrence of multiple faults， a scheduling model of emergency repair team based on the minimum maintenance time is established. By introducing dynamically adjusted inertia weights to improve the Particle Swarm Optimization（PSO）， the local convergence and global convergence of the algorithm are improved. At the same time， for the Traveling Salesman Problem（TSP）， the algorithm's ability to solve high-dimensional and sequential optimization problems is enhanced. A simulation study of a 50-node calculation example in a city shows that the proposed method can accurately and effectively determine the location of the emergency repair team in the early stage. When multiple faults occur in the power grid， it can quickly and efficiently determine the emergency repair sequence plan， reduce the deployment time of emergency repair work， and reduce losses caused by power outages. |
| keywords:station selection repair order inertial weight TSP PSO |
| 查看全文 查看/发表评论 下载PDF阅读器 |
