基于气体检测的空气开关柜绝缘缺陷识别

Insulation defect recognition of air switchgear based on gas detection

为了识别开关柜内的绝缘缺陷,设计了绝缘缺陷实验模型以获取训练样本,建立了具有自学习能力的BP(back propagation)神经网络并引入遗传算法进行网络优化,神经网络输入层为开关柜内气体特征,输出层为开关柜内绝缘缺陷,建立了开关柜内气体特征与绝缘缺陷的映射关系。经过样本训练,优化后的神经网络能够准确地识别绝缘缺陷。

In order to distinguish the insulation defect in switchgear, models of insulation defects are designed to obtain training samples, BP(back propagation) neural network with self-learning ability is established and genetic algorithm is used to optimize network. The input layer of neural network is the gas characteristic in switchgear,and the outer layer is insulation defects in switchgear. The mapping relationship between gas characteristics and insulation defects in switchgear is established. After samples training, the neural network can reliably distinguish the types of insulation defects and realize the insulation defects monitoring of air switchgear.