基于DGA与TPE-LightGBM的变压器故障诊断

Transformer fault diagnosis based on DGA and TPE-LightGBM

油中溶解气体分析(dissolved gas analysis,DGA)对变压器故障的早期预警及诊断具有重要意义。为了提升变压器故障诊断的准确性及可靠性,提出一种基于树结构概率密度估计(tree-structured parzen estimator,TPE)算法优化轻量级梯度提升机(light gradient boosting machine,LightGBM)的变压器故障诊断方法。首先,建立包含油中气体比值、编码等16维DGA特征集合,采用最小绝对收缩和选择(least absolute shrinkage and selection opera-tor,LASSO)算法选择用于变压器故障诊断的有效特征量;其次,构建基于LightGBM的变压器故障诊断方法,并引入TPE算法对LightGBM诊断模型参数进行优化,形成最优故障诊断模型;最后,选用精确度、召回率和F1分数等评价指标对所提诊断模型性能进行评估。研究结果表明,TPE-LightGBM的平均准确率为90.23%,其诊断精度及鲁棒性均优于RF和XGBoost等算法。同时,与现场常用的三比值法进行对比,所提方法的准确性和可靠性均有显著提升。该方法可有效提升电力变压器的智能运维水平。

Dissolved gas analysis(DGA)is significant for early warning and diagnosis of transformer faults.To enhance the accuracy and reliability of transformer fault diagnosis,a transformer fault diagnosis method is proposed based on the tree-structured parzen estimator(TPE)algorithm to optimize the light gradient boosting machine(LightGBM).Firstly,a 16-dimensional DGA feature set including gas ratios and encodings in oil is established,and the least absolute shrinkage and selection operator(LASSO)algorithm is used to select effective feature quantities for transformer fault diagnosis.Secondly,a transformer fault diagnosis method based on LightGBM is constructed,and the TPE algorithm is introduced to optimize the parameters of the LightGBM diagnosis model,forming an optimal fault diagnosis model.Finally,evaluation metrics such as accuracy,recall,and F1 score are selected to assess the performance of the proposed diagnosis model.The research results indicate that the average accuracy of TPE-LightGBM is 90.23%,and its diagnostic accuracy and robustness are superior to algorithms such as RF and XGBoost.At the same time,compared with the commonly used three-ratio method in practice,the proposed method shows significantly improved accuracy and reliability.This method can effectively enhance the level of intelligent operation and maintenance of power transformers.