人工鱼群算法在金属氧化物避雷器在线监测中的应用

Application of Artificial Fish Swarm Algorithm in On-Line Monitoring of Metal Oxide Arrester

针对金属氧化物避雷器(MOA)老化在线监测问题,提出一种基于人工鱼群算法(AFSA)的MOA在线监测技术。利用AFSA较好的寻优能力,求解MOA等效模型中反应MOA老化情况的,k,c值,从而实现对其的老化监测,并通过Matlab仿真分析谐波电压含量及其初相位对本文算法的影响。仿真分析表明:AFSA可较好的将模型中的计算泄漏电流拟合逼近实际测量的泄漏电流。此外,该算法求解的模型参数α,k,c值的最大误差分别为0.04%,0.02%,0.04%,表明算法对电网系统运行电压中谐波电压含量及其初相位具有较好的抗干扰性,验证了本文算法的可靠性,提高了MOA在线监测的准确性。

In order to solve the on-line aging monitoring of metal oxide arrester（MOA）, a technology about monitoring the MOA degradation based on the artificial fish swarm algorithm（AFSA） is proposed.Based on the AFSA, the parameters of α, k and c in the equivalent model are computed which will vary during the lifetime of the MOA so as to monitor the degradation of MOA. Moreover, according to the simulations, the influence of the operating voltage harmonics in power grid on the algorithm is also analyzed. The studies show that the monitoring technology based on AFSA can fit the calculated leakage current in the model to the actual measured leakage current of MOA. Additionally, the algorithm in this paper can solve the parameters, k and c in the model and the maximum errors are 0.04%, 0.02%, and0.04% respectively. The result demonstrates that the content of voltage harmonics and its initial phase have low influence on the proposed algorithm, and the effectiveness of the algorithm is verified. Finally,the result of MOA on-line monitoring is improved.