油浸式变压器故障智能诊断应用研究

Applied research on intelligent diagnosis of oil-immersed transformer faults

在电网系统中,油浸式变压器出现故障会导致供电线路受到影响,传统人工检测不能满足变压器故障检测需要,为此,开展油浸式变压器智能故障诊断应用研究。以某变电站变压器为例,选择BP和QIA-BP两种神经网络算法开展油浸式变压器故障诊断研究。比较BP神经网络检测与人工检测的效率和准确率,并将QIA-BP神经网络与BP神经网络两种算法对析出气体的体积分数跟踪效果进行对比。通过白盒测试检验QIA-BP智能诊断系统的准确性,验证了QIA-BP神经网络智能故障检测对于变压器故障诊断的有效性和优越性。

In the power grid system,the fault of oil-immersed transformer can affect the power supply lines.The traditional manual testing cannot meet the needs of transformer fault detection.Therefore,research on the application of intelligent fault diagnosis for oil-immersed transformer is carried out.Taking a substation transformer as an example,two neural network algorithms,BP and QIA-BP,are selected to carry out oil-immersed transformer fault diagnosis research.The efficiency and accuracy of BP neural network detection are compared with those of manual detection,and the tracking effect of QIA-BP neural network is compared with that of BP neural network.The accuracy of the QIA-BP intelligent diagnosis system is tested by white box test,which verifies the effectiveness and superiority of QIA-BP neural network intelligent fault detection for transformer fault diagnosis.