摘要
随着大量的分布式电源接入配电网,使得电力系统故障时故障电流的大小和方向都会发生变化,传统的故障定位方法将不再适用。为了快速高效地解决含分布式电源的故障区段定位问题,引入一种基于模拟退火和进化机制改进的人工鱼群算法(evolving-simulated annealing artificiat fish swarm algorithm,E-SAAFSA)。该算法既保留了基本人工鱼群算法的精确度,同时又具备模拟退火算法跳出局部最优的能力以及生物进化机制"优胜劣汰"的高选择性。最后采用经典的33节点网络作为模型,Matlab2017b软件对算例进行仿真。结果表明算法通过对人工鱼的进化、鱼群行为的模拟以及对最优人工鱼"退火"的处理,可以实现配电网的故障区段定位,且具有稳定性好、精确性高、收敛速度快等特点。
With access of a large number of distributed generations to the distribution network,the magnitude and direction of the fault current will change in the case of fault of power system and the traditional fault location method will no longer be applicable. For solving quickly and efficiently location problem of fault sections of the distributed generation,an kind of evolving-simulated annealing artificial fish swarm algorithm(E-SAAFSA)based on simulated annealing and evolution mechanism is introduced,which not only retains the accuracy of the basic artificial fish swarm algorithm and,at the same time,but also has the ability of the simulated annealing algorithm to jump out of the local optimum and the high selectivity of the biological evolution mechanism survival of the fittest. Finally,a classic 33-node network is used as the model and Matlab2017 b software is used to simulate the calculation example. The results show that the algorithm can achieve the location of fault section of the distribution network by simulating the evolution of the artificial fish,the behavior of the fish swarm and the annealing of the optimal artificial fish,and has such characteristics as good stability,high accuracy and fast convergence speed.
作者
管恩齐
何晋
骆通
周石金
杨凡
曹鲁成
GUAN Enqi;HE Jin;LUO Tong;ZHOU Shijin;YANG Fan;CAO Lucheng(School of Electrical Information Engineering,Yunnan Minzu University,Kunming 650000,China;Shan County Power Supply Company of State Grid Shandong Electric Power Company,Shandong Heze,274300,China)
出处
《电力电容器与无功补偿》
2022年第1期102-110,共9页
Power Capacitor & Reactive Power Compensation
基金
国家自然科学基金项目(61365007,61761049)
云南省教育厅科学研究基金项目(2020Y0242)。
关键词
鱼群算法
模拟退火
分布式电源
fish swarm algorithm
simulated annealing
distributed generation
