| 引用本文: | 张又文,冯斌,陈页,廖伟涵,郭创新.基于遗传算法优化XGBoost的油浸式变压器故障诊断方法[J].电力自动化设备,2021,41(2): |
| ZHANG Youwen,FENG Bin,CHEN Ye,LIAO Weihan,GUO Chuangxin.Fault diagnosis method for oil-immersed transformer based on XGBoost optimized by genetic algorithm[J].Electric Power Automation Equipment,2021,41(2): |
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| 摘要: |
| 为了提高油浸式变压器故障诊断的精度及可靠性，研究了一种基于遗传算法优化极端梯度提升(XGBoost)的油浸式变压器故障诊断方法。首先，以油中溶解气体分析(DGA)为依据，采用无编码比值方法提取油浸式变压器的9维故障特征，并对数据样本进行归一化处理；以归一化样本为输入建立基于XGBoost的故障诊断模型，并采用遗传算法对模型中的多个超参数同时进行优化。在算例部分，收集547例故障类型确定的DGA数据进行对比实验，结果表明与现有传统方法相比，所提方法的诊断精度和稳定性有显著提升；同时验证了遗传算法对故障诊断模型的优化提升效果。 |
| 关键词: 油中溶解气体分析 变压器 故障诊断 极端梯度提升 遗传算法 |
| DOI：10.16081/j.epae.202012021 |
| 分类号:TM41 |
| 基金项目:国家电网公司科技项目(52110418000T) |
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| Fault diagnosis method for oil-immersed transformer based on XGBoost optimized by genetic algorithm |
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ZHANG Youwen, FENG Bin, CHEN Ye, LIAO Weihan, GUO Chuangxin
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College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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| Abstract: |
| In order to improve the accuracy and reliability of fault diagnosis for oil-immersed transformer, the fault diagnosis method for oil-immersed transformer based on XGBoost(eXtreme Gradient Boosting) optimized by GA(Genetic Algorithm) is studied. Firstly, based on DGA(Dissolved Gas Analysis),the non-coding method is used to extract the 9-dimensional fault characteristics of oil-immersed transformer, and the data samples are normalized. The fault diagnosis model based on XGBoost is built with the normalized samples as inputs, and the hyperparameters in the model are simultaneously optimized by GA. In case study, 547 samples of DGA data determined by fault types are collected for comparison experiments. Results show that the diagnosis accuracy and stability of the proposed method are significantly improved compared with the existing traditional methods. The optimization effect of GA on the fault diagnosis model is verified at the same time. |
| Key words: dissolved gas analysis electric transformers fault diagnosis XGBoost genetic algorithms |
