| 引用本文: | 李 强,陈 潜,武霁阳,等.基于集成学习的高压直流输电系统故障诊断[J].电力系统保护与控制,2023,51(16):168-178.[点击复制] |
| LI Qiang,CHEN Qian,WU Jiyang,et al.Ensemble learning-based HVDC systems fault diagnosis[J].Power System Protection and Control,2023,51(16):168-178[点击复制] |
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| 摘要: |
| 以某西南电网变电站出现的4种故障的实测数据作为数据集，针对高压直流输电(high voltage direct-current, HVDC)系统的故障诊断设计出一种基于集成学习(ensemble learning, EM)的故障诊断方法，显著提升了故障诊断的速度、精度和鲁棒性。首先，对4类故障数据进行数据预处理，同时对故障数据的特征进行提取并完成训练，使用故障数据标签对故障数据集进行均分权重。然后，计算当前弱分类器对带权重数据集的分类误差，并计算当前分类器在强分类器中的权重。最后，更新训练样本权值的分布得到强分类器，根据训练好的模型对不同数据集下的故障类型进行辨识实验。通过与BP神经网络故障诊断模型对比，所提出的方法在多组测试中可以达到89%以上的诊断准确率，错误率较低并且鲁棒性强，有利于HVDC系统的故障识别和快速诊断。 |
| 关键词: 高压直流输电系统 故障诊断 集成学习 分类器 |
| DOI：10.19783/j.cnki.pspc.221653 |
| 投稿时间：2022-10-18修订日期：2022-11-06 |
| 基金项目:国家自然科学基金项目资助(62263014)；南方电网重点科技项目资助(CGYKJXM20210309) |
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| Ensemble learning-based HVDC systems fault diagnosis |
| LI Qiang1,CHEN Qian1,WU Jiyang1,PENG Guangqiang1,HUANG Xionghui1,LI Ziyou2,YANG Bo2 |
| (1. China Southern Power Grid EHV Transmission Company, Guangzhou 510000, China; 2. China Southern
Power Grid EHV Transmission Company Dali Bureau, Dali 671000, China) |
| Abstract: |
| In this paper, the measured data of four kinds of faults in a substation of the Southwest power grid are used as the data set. A method based on ensemble learning (EM) is designed for the fault diagnosis of a high voltage direct-current (HVDC) system, one which can significantly improve the speed, accuracy, and robustness of fault diagnosis. First, data preprocessing for the four types of fault data is conducted. At the same time, the feature of fault data is extracted and trained. The fault data label is used to average the weight of the fault data set. Then the classification error of the current weak classifier for the weighted data set is calculated, as well as the weight of the current classifier in the strong classifier. Finally, the distribution of the weights of the training samples is updated to obtain a strong classifier. From the trained model, the fault types are identified in the different data sets. Compared with a back propagation neural network fault diagnosis model, the proposed method can achieve more than 89% diagnostic accuracy in multiple tests, with a low error rate and strong robustness. It is conducive to fault identification and rapid diagnosis in an HVDC system in operation. |
| Key words: HVDC transmission system fault diagnosis ensemble learning classifier |
