为进一步提高局部电网在故障恢复过程中的高效性和可靠性，文章提出了一种基于改进GSA-GWO算法的局部电网故障下孤岛划分策略。首先，采用最优-最劣法对负荷进行评价，得到负荷权重值，从而确定局部电网故障下孤岛划分重要负荷恢复优先级;其次，结合负荷优先级确定负荷等级权重系数值，构建含分布式电源的局部电网孤岛划分目标函数模型;再次，为了获取更佳的目标函数求解结果，引入混沌反向学习和遗传退火算法(Genetic Simulated An-nealing，GSA)对灰狼优化算法(Grey Wolf Optimization，GWO)进行改进，以提高算法的优化性能;最后，以修改后的IEEE 69 节点为例进行仿真分析，运用改进的GSA-GWO算法对局部配电网故障模型进行求解，得到更佳的孤岛划分结果。算例分析表明，文章提出的策略可准确实现局部电网故障下孤岛划分最优策略，保证了重要负荷的电力供应，验证了策略的有效性和优越性。
Island partition strategy of local power grid with distributed generation based on improved GSA-GWO algorithm
In order to further improve the efficiency and reliability of the local power grid in the process of fault recovery,this paper proposes an island partition strategy of local power grid with distributed generation based on improved GSA-GWO algorithm.Firstly,the optimal-worst method is used to evaluate the load to obtain the load weight value,so as to determine the priority of island division of important load restoration under local power grid fault.Secondly,combined with the load priority,the load level weight coefficient is determined,and the objective function model of island division of local power grid with distributed generation is constructed.Then,in order to obtain better objective function solution results,chaotic reverse learning and genetic annealing algorithm(GSA)are introduced to improve the grey wolf optimization algorithm(GWO)to improve the optimization performance of the algorithm.Finally,the modified IEEE 69 node is taken as an example for simulation analysis.The improved GSA-GWO algorithm is used to solve the local distribution network fault model,and a better islanding result is obtained.The example analysis shows that the strategy proposed in this paper can accurately realize the optimal strategy of island division under local grid fault,ensure the restoration of power supply of important loads,and verify the effectiveness and superiority of the strategy.
local power griddistributed power systemisland partitionpower supply restored
国家科技期刊平台
NSTL
万方数据
维普
