| 引用本文: | 欧阳鑫,李志斌.基于样本扩充和特征优选的IGWO优化SVM的变压器故障诊断技术[J].电力系统保护与控制,2023,51(18):11-20.[点击复制] |
| OUYANG Xin,LI Zhibin.Transformer fault diagnosis technology based on sample expansion and feature selection and SVM optimized by IGWO[J].Power System Protection and Control,2023,51(18):11-20[点击复制] |
|
| 摘要: |
| 为了增强变压器故障诊断模型对不平衡样本的学习能力从而提高少数类故障样本的识别精度，提出了一种基于样本扩充和特征优选的融合多策略改进灰狼算法(improved grey wolf optimizer with multi-strategy, IGWO)优化支持向量机(support vector machine, SVM)的变压器故障诊断技术。首先，使用基于K最近邻过采样方法及核密度估计自适应样本合成算法的混合过采样技术对少数类样本进行扩充得到均衡数据集，并在此基础上采用方差分析对变压器候选比值征兆进行特征优选。然后，通过改进灰狼优化算法(grey wolf optimizer, GWO)初始化策略、参数及位置更新公式，并引入差分进化策略调整种群，提出了融合多策略的改进灰狼算法。最后，构建了一种基于混合过采样技术的IGWO优化SVM的变压器故障诊断模型，并通过多组对比实验验证了所提方法能够有效增强模型对少数类故障样本的识别能力，并提升模型的整体分类性能。 |
| 关键词: 变压器故障诊断 不平衡数据集 混合过采样 特征优选 改进灰狼算法 支持向量机 |
| DOI：10.19783/j.cnki.pspc.230205 |
| 投稿时间：2023-03-01修订日期：2023-06-27 |
| 基金项目:国家自然科学基金项目资助(51405286)；上海市青年科技英才杨帆计划资助(20YF1414800)；上海市电站自动化技术重点实验室项目资助(13DZ2273800) |
|
| Transformer fault diagnosis technology based on sample expansion and feature selection and SVM optimized by IGWO |
| OUYANG Xin,LI Zhibin |
| (College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China) |
| Abstract: |
| To enhance the learning ability of a transformer fault diagnosis model for unbalanced samples and improve the recognition accuracy of minority fault samples, a transformer fault diagnosis technology based on sample expansion and feature optimization and support vector machine (SVM) optimized by improved grey wolf optimizer (GWO) with multi-strategy (IGWO) is proposed. First, the mixed oversampling technique based on K-nearest neighbor oversampling approach and kernel based adaptive synthetic algorithm is used to expand the minority samples to obtain the balanced datasets,, and analysis of variance (ANOVA) is used to select the transformer candidate ratio features. Then, by improving the initialization strategy and update formulas of parameters and positions of the GWO and introducing a differential evolution strategy to adjust populations, an improved GWO with multi-strategy is proposed. Finally, a transformer fault diagnosis model based on mixed oversampling technology and SVM optimized by IGWO is constructed, and experimental results show the method can enhance the recognition accuracy of the model for minority fault samples and improve the overall classification performance of the model effectively. |
| Key words: transformer fault diagnosis unbalanced datasets mixed oversampling feature selection improved grey wolf optimizer support vector machine |
