基于细菌觅食算法优化的电力变压器故障诊断技术

针对支持向量机(SVM)分类性能受参数影响,且最优参数难以获取这一问题,提出一种基于细菌觅食算法(BFA)的电力变压器故障诊断模型的参数寻优方法。该方法以电力变压器油中特征气体含量作为状态评价样本,通过BFA寻找全局最优SVM参数解,构建k-折平均分类准确率目标函数,建立变压器故障诊断模型。仿真结果表明,BFA对SVM最优参数的选取较遗传算法(GA)、粒子群算法(PSO)更迅速,且优化后的SVM电力变压器故障诊断模型具有更高的精确度;利用BFA优化方法建立的SVM电力变压器状态诊断模型,对IEC三比值法中无法判断的数据也可进行精确诊断。最后,通过实例分析,验证了方法的有效性。

Fault diagnosis technology of transformer based on bacterial foraging algorithm optimization

Aiming at the problems that the classification performance is affected by parameters and the optimal parameters are difficult to obtain, a parameter optimization method based on bacterial foraging algorithm (BFA) for power transformer fault diagnosis model is proposed. The gas contents of transformer oil were collected as evaluation samples, the best global optimal solution of SVM was searched by BFA, the objective function of k-fold average classification accuracy rate was constructed, and the analysis model of optimal SVM power transformer were established. The simulation results show that the selection of SVM optimal parameters by BFA is more rapid than that by using genetic algorithm (GA) and particle swarm optimization (PSO), and the optimized SVM power transformer fault diagnosis model has higher accuracy. The SVM state diagnosis model of power transformer which is established by BFA optimization method can accurately diagnose the data that cannot be diagnosed by IEC three ratio method. Finally, the effectiveness of the method is verified by an example analysis.