基于灰色理论及BP神经网络的变压器油击穿电压预测方法

Grey theory and BP neural network-based prediction technique for transformer oil breakdown voltage

变压器油击穿电压的预测,对于变压器的故障诊断和日常维护具有重要的意义。本文提出了1种击穿电压多参数关联预测方法,即通过对变压器油日常监督数据进行灰色关联分析,挖掘与击穿电压存在强关联性的指标;利用BP网络建立击穿电压与4个强关联性指标的关系模型;采用模糊C均值聚类算法聚类原始样本,以聚类中心训练网络,以解决大样本情况引起的网络结构复杂、收敛性及泛化能力差等神经网络固有问题。仿真结果表明,预测模型精度较高,预测值与实际值的相对误差均在10%以内,能够满足实际应用要求,具有重要的应用价值。

Prediction of breakdown voltage of transformer oil has a great significance to the fault diagnosis,daily maintenance of transformer.This paper proposed a prediction method of breakdown voltage via multi-parameter correlation.Through examining the routine monitoring data of transformer oil by gray correlation analysis,some parameters which have strong correlation with breakdown voltage were excavated,then a relational model of breakdown voltage and those parameters was further constructed using back-propagation neural network.The clustering centers used to train network were acquired through clustering the original monitoring data samples with fuzzy C-means clustering algorithm.This method can resolve natural problems of neural networks caused by large sample capacity,such as complication of net construction,inferior astringency,poor generalization ability,and so on.Test results show that the precision of the prediction model is high and that the relative errors between predicted values and real values are all less than 10%,which indicates the significant practical value of the model.