电力系统故障诊断的萤火虫和粒子群混合算法研究

Research on Mixture Algorithm of Glowworm and Particle for Power Fault Section Estimate

摘要:

电力系统故障诊断主要就是根据保护和断路器的动作信息来判别故障区域,而找出故障元件又是其难点和主要工作,以目标函数描述其模型,则故障诊断问题转化为0-1整数规划问题。适合于智能算法求解。用粒子群算法解决该问题时收敛速快,但容易陷入局部最优值；用萤火虫算法时能够找到全局最优值,但其后期收敛速度较慢。论文融合这两种算法并用之求解故障诊断的目标函数,仿真结果表明：融合后的算法兼备两种算法的优点,能够以较快速度收敛,并找到全局最优解,且收敛精度高,稳定性好。

Abstract:

The main basement of fault diagnose in power system is estimate the fault location by making use of the informations from the actions of circuit breakers and protective relays, and find out the fault elements is the main job, which also is difficult, describe the question as a target function, then turn the fault diagnose to a 0-1 integer programming model. This kind of question is easy to solve by intelligent algorithm。Particle optimization algorithm approach converge is fast when solving the problem, but it’s easy to fall into local optimum。Glowworm swarm optimization algorithm can find out the best value while it’s a little slower than particle optimization algorithm. The paper make full use of the two algorithm’s advantages, combine them and lead them in power system fault diagnose. the simulation results prove that the new idea is successfully, it has a high approach converge and can find out the best value while precise and stability.