基于DGA与鲸鱼算法优化Logit Boost-决策树的变压器故障诊断方法

为对变压器进行准确的故障诊断，将油中溶解气体分析(dissolved gasses analysis, DGA)与人工智能技术相结合，提出了一种基于鲸鱼优化算法(whale optimization algorithm, WOA)优化LogitBoost-决策树的变压器故障诊断模型。该模型以决策树作为弱学习器，通过将Logit Boost集成算法作为集成框架使多个决策树集成为一个强学习器，并构建了一种基于鲸鱼优化算法的优化策略去优化LogitBoost-决策树模型中的决策树棵数及决策树的最大分裂次数。实验表明，所构建的WOA-Logit Boost-DT变压器诊断模型与常用的决策树、支持向量机、三比值等诊断模型相比，综合诊断精度分别提高了约4%、10%、21%。所构建的相关模型能为变压器的故障诊断提供技术支持。

Transformer fault diagnosis based on DGA and a whale algorithm optimizing a LogitBoost-decision tree

To diagnose a transformer fault accurately, dissolved gas analysis (DGA) is combined with artificial intelligence technology, such that a transformer fault diagnosis model based on LogitBoost-decision tree optimized by whale optimization algorithm (WOA) can be obtained. The model takes the decision tree as the weak learner. The LogitBoost ensemble algorithm is used as an integration framework to integrate multiple decision trees into a strong learner. A model optimization strategy based on the whale optimization algorithm is constructed to optimize the decision tree and the maximum splitting times of the decision tree in the LogitBoost-decision tree model. The experiments show that the WOA-LogitBoost-DT transformer diagnosis model improves the comprehensive diagnosis accuracy by about 4%, 10% and 21% compared with the commonly used decision tree, support vector machine and three ratio diagnosis models, respectively. The proposed model can provide technical support for transformer fault diagnosis.