改进GA-BP算法在变压器色谱故障诊断中的应用

Application of Improved GA-BP Algorithm in Power Transformer Diagnosis Using Chromatographic Method

利用遗传算法的全局搜索性能和BP算法较强的局部搜索能力,提出一种收敛速度快的改进GABP混合算法,并应用于油中溶解气体分析的电力变压器色谱故障诊断中.实际结果表明了这种混合算法对电力变压器故障诊断具有较快的收敛速度和较高的诊断精度.

The application of hybrid algorithm which combines improved genetic algorithm （GA） and error back-propagation algorithm in artificial neural network training is studied first. As the back-propagation neural network （BP-NN） is adopted to diagnose the dissolved gases of trans- former, convergence rate becomes slow, convergence precision becomes bad and even being out of convergence as more sampled data are trained and more complicated relation between input and out- put becomes. This paper presents a improved hybrid algorithm based on GA-BP which makes good use of searching virtue in overall range using genetic algorithm and great capability of searching in local range using error back-propagation algorithm. This hybrid algorithm is applied to transformer fault diagnosis. The superior efficiency of the method has been verified by the simulation results of practical examples.