基于改进烟花算法的配电网集中式馈线自动化故障定位研究Research on Fault Location of Centralized FA Based on Improved Fireworks Algorithm
裘德玺,宋哲,冷磊磊,卢丽胜
QIU Dexi,SONG Zhe,LENG Leilei,LU Lisheng
摘要(Abstract)：
配电网是整个电网中结构最复杂和事故最频发的部分之一。因此,配电网故障的快速、准确定位成为重中之重。群智能算法在配电网故障定位中具有良好的应用效果,其中烟花算法以参数少、执行过程简单、实现容易、鲁棒性强等特点,在处理要求抗局部最优的优化问题上具有一定优势。针对烟花算法收敛较慢的缺陷,采用随机性原则作为新的保留机制,然后利用模拟退火算法增强烟花算法的抗局部最优的能力。经测试线路验证,证明改进后的烟花算法适用于配电网故障的快速、准确定位,且具有不错的搜索速度和容错能力。
Distribution network is closely related to people's livelihood, and it is one of the most complex and frequent accidents in the whole power grid. The failure of distribution network will affect the user's electricity experience, and even cause damage to power equipment and casualties. In the actual production process,there will be abnormal equipment, the final failure of fault location, resulting in a more serious power failure.Therefore, the rapid positioning after the failure of distribution network becomes the top priority. The swarm intelligence algorithm has a good application effect in distribution network fault location, but it has a large amount of calculation, slow convergence speed and easy to fall into local optimum. Fireworks algorithm, as a swarm intelligence algorithm, stands out because of its fewer parameters, simple execution process, easy implementation, strong robustness and other characteristics, and has certain advantages in the optimization problems that require resistance to local optimization. Aiming at the slow convergence of fireworks algorithm,the randomness principle is used as a new retention mechanism, and then simulated annealing algorithm is used to enhance the anti local optimal ability of fireworks algorithm. The test results show that the improved fireworks algorithm is suitable for fast and accurate location of single and multiple faults in distribution network, and has good search speed and fault tolerance ability.
关键词(KeyWords)：
群智能算法;配电网;烟花算法;故障定位
swarm intelligence algorithm;distribution network;fireworks algorithm;fault location
基金项目(Foundation):
作者(Author):
裘德玺,宋哲,冷磊磊,卢丽胜
QIU Dexi,SONG Zhe,LENG Leilei,LU Lisheng
DOI: 10.19585/j.zjdl.202109014
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