Practical features of dissolved gases analysis (DGA) are selected and proposed from 62 key gases combinations through maximal information coefficient (MIC) to minimize the influences of random errors and relative percentages variation for field application. Then the Pearson correlation coefficient is employed to filter and optimize the feature set to reduce the redundancy of the selected features. Lasso regression is proposed to build a multi-dimension linear model of the selected features. In the multi-dimension model, the position in which the parameter changes drastically is defined as a change point, which contains specific information on the transformer's operation status. The case analysis demonstrates that the variation of selected features under abnormal status can be figured out from that of normal status prior to fault occurrence. The change point detection based on Lasso regression shows the least number of days between change point and time of failure and standard deviation (SD), which accurately reflects the location of the transformer fault in most scenarios. Therefore, the proposed technique provides an available approach for the dynamic fault prediction based on the dissolved gas data, showing the advantage of robustness, data-free training, and early warning.

中文翻译：

---

基于Lasso回归和溶解气体分析变化点检测的电力变压器动态故障预测

通过最大信息系数（MIC）从62种关键气体组合中选择并提出溶解气体分析（DGA）的实用特征，以尽量减少现场应用的随机误差和相对百分比变化的影响。然后利用Pearson相关系数对特征集进行过滤和优化，减少所选特征的冗余度。提出套索回归来构建所选特征的多维线性模型。在多维模型中，将参数剧烈变化的位置定义为变化点，其中包含变压器运行状态的具体信息。案例分析表明，异常状态下所选特征的变化可以从故障发生前的正常状态中找出。基于Lasso回归的变化点检测显示变化点与故障时间和标准差（SD）之间的最少天数，在大多数场景下准确反映了变压器故障的位置。因此，所提出的技术为基于溶解气体数据的动态故障预测提供了一种可用的方法，显示了鲁棒性、无数据训练和早期预警的优势。