网状电力系统规划的元启发式算法比较
Comparison of Meta-Heuristics for the Planning of Meshed Power Systems
摘要：
电力系统规划任务是一个组合优化问题。目标函数最小化的经济成本受到一组技术和操作的限制。元分析通常被用作优化策略，通过结合切换、线路加固或新线路措施来找到该问题的解决方案。常见的算法有GA、PSO、HC、ILS或更新的方法，如GWO或FWA。在本文中，我们比较这些算法在同一框架内。我们测试每个算法8个不同的测试网格，从73到9421总线。对于每个网格和算法，我们开始运行50次，最长运行时间为1小时。结果表明，算法的性能取决于初始网格状态、网格大小和度量的数量。ILS方法在大多数情况下是非常稳健的。在更大的测试网格中，更多的探索性算法，例如，遗传算法和粒子群算法，在更短的运行时间内找到解决方案。
英文摘要：
The power system planning task is a combinatorial optimization problem. The objective function minimizes the economic costs subject to a set of technical and operational constraints. Meta-heuristics are often used as optimization strategies to find solutions to this problem by combining switching, line reinforcement or new line measures. Common heuristics are GA, PSO, HC, ILS or newer methods such as GWO or FWA. In this paper, we compare these algorithms within the same framework. We test each algorithm on 8 different test grids ranging from 73 to 9421 buses. For each grid and algorithm, we start 50 runs with a maximum run time of 1 hour. The results show that the performance of an algorithm depends on the initial grid state, grid size and amount of measures. The ILS method is very robust in most cases. In the larger test grids, more exploratory heuristics, e.g., GA and PSO, find solutions in shorter run times.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
