改进支持向量机在电力变压器故障诊断中的应用研究

针对电力变压器故障诊断中状态量判断指标过于绝对、智能算法准确率受参数影响等问题,在分析电力变压器故障的基础上,提出将支持向量机(Support Vector Machine,SVM)和细菌觅食算法(Bacterial Foraging Algorithm,BFA)相结合用于电力变压器的故障诊断方法。通过细菌觅食算法的寻优能力找到最优的支持向量机惩罚因子和核参数,提高了故障诊断能力。通过仿真和实例进行对比分析,验证了该方法的优越性。结果表明,相比于粒子群优化,细菌觅食算法具有更好的寻优能力。基于BFA-SVM的故障诊断模型,相比于改进前,具有更高的准确性、鲁棒性和寻优能力,故障诊断准确率相比于粒子群优化提高了7.50%,具有一定的实用价值。

Application of improved Support Vector Machine in power transformer fault diagnosis

Aiming at the problems that the judgment index of state quantity is too absolute and the accuracy of intelligent algorithm is affected by parameters in power transformer fault diagnosis. Based on the analysis of power transformer fault, a method combining Support Vector Machine (SVM) and Bacterial Foraging Algorithm (BFA) is proposed for power transformer fault diagnosis. Through the optimization ability of bacterial foraging algorithm, the optimal penalty factor and kernel parameters of support vector machine are found to improve the ability of fault diagnosis. The superiority of this method is verified by simulation and example. The results show that, compared with particle swarm optimization, bacterial foraging algorithm has better optimization ability, the fault diagnosis model based on bfa-svm has higher accuracy, robustness and optimization ability than before, compared with particle swarm optimization, the accuracy of fault diagnosis is improved by 7.50%, which has certain practical value.