基于改进二进制蛇优化算法的配电网故障定位

Method of Distribution Network Fault Location Based on Improved Binary Snake Optimization Algorithm

分布式电源(distributed generation,DG)大规模接入给配电系统带来更多不确定性、随机性,系统运行方式更复杂,传统故障定位方法难以适应新型电力系统构建。提出了一种基于改进二进制蛇优化算法(improved binary snake optimization,IBSO)的新型故障区段定位方法。利用SPM混沌映射生成高质量的随机数序列,以提高算法种群中个体的随机性,并引入了遗传算法的动态变异策略,根据不同的搜索状态和进化阶段来调整变异率和变异方式,提高算法的灵活性和准确性。通过仿真证明,该方法适用于在含有分布式电源的配电网中定位单一和多重故障区段,相比蛇优化算法、传统二进制粒子群算法以及遗传算法在收敛性、快速性和准确性方面更优。

With the integration of DGs(distributed generations)into the distribution network,the traditional single-source radial network is transformed into a complex network with multiple power sources.Traditional fault location methods are difficult to adapt to the construction of complex operating conditions.A novel fault segment localization method based on IBSO(improved binary snake optimization)algorithm was proposed.The SPM chaotic mapping was used to generate high-quality random number sequences,which improves the randomness of individuals in the algorithm population.A dynamic mutation strategy based on genetic algorithms was also introduced.According to different search states and evolutionary stages,the mutation rate and mode were dynamically adjusted.The flexibility and accuracy of the algorithm were improved significantly.The simulation results show that the proposed method is suitable for locating single and multiple fault sections in distribution networks with distributed generation.It has better convergence,speed,and accuracy compared with the snake optimization algorithm,traditional binary particle swarm algorithm,and genetic algorithm.