| 引用本文: | 罗国敏,杨雪凤,尚博阳,等.基于改进堆叠降噪自编码器的配电网高阻接地故障检测方法[J].电力系统保护与控制,2024,52(24):149-160.[点击复制] |
| LUO Guomin,YANG Xuefeng,SHANG Boyang,et al.High impedance grounding fault detection method of a distribution network based on an improved stacked denoised autoencoder[J].Power System Protection and Control,2024,52(24):149-160[点击复制] |
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| 摘要: |
| 针对配电网高阻故障判定阈值选取难、噪声影响大和识别精度低等问题，提出了一种基于改进堆叠降噪自编码器的高阻接地故障检测方法，从特征提取及网络模型两个层面增强检测方法的可靠性与抗噪性能。首先，结合时频数据处理手段刻画高阻接地故障与正常工况的物理特性差异，为构建故障样本特征库提供理论依据；其次，通过皮尔逊相关系数对时域、频域和时频域的故障特征进行分析与筛选，从而构造多域特征融合样本库，避免特征冗余现象；然后，利用极限学习机的强高维特征分类特性对堆叠降噪自编码器模型进行改进，以提高高阻接地故障分类器的鲁棒性和准确性；最后，在Matlab/Simulink中搭建10 kV配电网仿真模型进行算例分析。结果表明，该方法在-1 dB强噪声条件下仍有95.57%的高阻故障检测准确率，具有较高的工程实用价值。 |
| 关键词: 配电网 高阻接地故障 多域特征融合 堆叠降噪自编码器 极限学习机 |
| DOI：10.19783/j.cnki.pspc.240534 |
| 投稿时间：2024-05-02修订日期：2024-10-01 |
| 基金项目:国家自然科学基金项目资助(U23B6007 |
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| High impedance grounding fault detection method of a distribution network based on an improved stacked denoised autoencoder |
| LUO Guomin,YANG Xuefeng,SHANG Boyang,LUO Simin,HE Jinghan,WANG Xiaojun |
| (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China) |
| Abstract: |
| A novel approach based on an improved stacked denoised autoencoder for detecting high impedance faults in distribution networks is proposed. This aims to tackle challenges such as the selection of suitable thresholds, susceptibility to high noise levels, and low recognition accuracy. This method enhances reliability and noise resistance through two key avenues: feature extraction and network model refinement. First, by integrating time-frequency data analysis techniques, the method captures distinctive physical characteristics distinguishing high impedance grounding faults from normal operational states. This forms the foundation for constructing a fault sample feature library. Secondly, fault features across time, frequency, and time-frequency domains are filtered using Pearson correlation coefficients to create a streamlined multi-domain feature fusion sample library, reducing redundancy and enhancing computational efficiency within the network model. Leveraging the strong high-dimensional feature classification capabilities of an enhanced extreme learning machine, the stacked denoising autoencoder model is refined to boost the robustness and accuracy of the high impedance grounding fault classifier. Finally, a Matlab/Simulink simulation model of a 10 kV distribution network is used for illustrative analysis. The results show that the method still has 95.57% accuracy of high impedance fault detection in the condition of –1 dB strong noise, which has high engineering practical value. |
| Key words: distribution network high impedance fault multi-domain feature fusion stacked denoised autoencoder extreme learning machine |
