基于等级偏好优序法和切负荷的配电网故障恢复

Distribution Network Service Restoration Based on Rank Preference Optimal and Load Shedding

评价方法决定着全局最优解的获得,对于群体智能算法至关重要。本文采用等级偏好优序法对种群中的个体进行评价,通过对优序法中的指标优序数进行改进,可将恢复方案的优劣程度更好地区分出来。针对传统群体智能算法难以应对负荷无法完全恢复的问题,本文提出了一种切负荷策略融入到二进制粒子群算法中,使得种群中的每个粒子为电气角度的可行解,通过算法的逐步迭代得到满足目标函数最优的解。并在故障恢复之前,采用重复潮流法对配电网络的供电能力进行评估,以此来判断算法是否需要投入切负荷,进而减少切负荷对算法效率的影响。最后通过算例验证了本文算法可以得到全局最优解并且可以解决负荷无法完全恢复的问题,具有一定的工程价值。

Evaluation method is essential for the swarm intelligence algorithm and determines the global optimal solution. In this paper, rank preference optimal was introduced to evaluate the individual of the population, which can get the obvious differences among alternatives by improving in attribute ordinal number. Regarding the traditional swarm intelligence algorithm is difficult to deal with the problem that the load cannot be recovered completely, this paper proposed a load shedding strategy to ensure each individual of the group is feasible in the point of electricity. In order to reduce the effect of load shedding on algorithm efficiency, repetitive power flow was introduced to calculate the power supply capability before executing the algorithm. Finally the analysis results show that the proposed method can effectively solve the problem of distribution network service restoration and has practical value.