| 引用本文: | 洪翠,连淑婷,黄晟,郭谋发.基于改进经验小波变换和改进多视角深度矩阵分解的直流配电网故障检测方案[J].电力自动化设备,2022,42(6): |
| HONG Cui,LIAN Shuting,HUANG Sheng,GUO Moufa.Fault detection scheme based on IEWT and IMDMF for DC distribution network[J].Electric Power Automation Equipment,2022,42(6): |
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| 摘要: |
| 为快速检测及可靠识别直流配电网故障，提出一种基于改进经验小波变换和改进多视角深度矩阵分解的直流配电网故障检测方案。通过最小二乘法非线性拟合故障电流局部的相频谱函数，基于此在一定的条件下修改经验小波函数的相频响应，使之尽可能与故障电流的局部相频特性相匹配；运用改进经验小波变换分解电流，计算细节分量c3的模极大值，构造故障检测判据；设计一种权重自学习网络，依据数据对分类任务的重要性分配不同的权重，嵌套于多视角深度矩阵分解模型前端，运用改进多视角深度矩阵分解模型对电流分量c1 — c3、极间电压udc这4个视角的数据进行故障特征提取，通过软分配层实现故障的分类。仿真测试结果表明，所提故障检测方案能够满足故障检测速动性、可靠性的要求，故障分类准确度高，为后续故障处理奠定了良好基础。 |
| 关键词: 直流配电网 故障检测与分类 改进经验小波变换 改进多视角深度矩阵分解 |
| DOI：10.16081/j.epae.202203016 |
| 分类号:TM713 |
| 基金项目:国家自然科学基金资助项目(51677030) |
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| Fault detection scheme based on IEWT and IMDMF for DC distribution network |
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HONG Cui1, LIAN Shuting1, HUANG Sheng2, GUO Moufa1
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1.College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China;2.College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
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| Abstract: |
| In order to quickly detect and reliably identify fault of DC distribution network, a fault detection scheme based on IEWT(Improved Empirical Wavelet Transform) and IMDMF(Improved Multi-view Deep Matrix Factorization) is proposed. The local phase-frequency spectra function of fault current is fitted nonlinearly by least square method, based on which, phase-frequency response of empirical wavelet function is modified under certain conditions to match the phase-frequency spectra characteristics of fault current as much as possible. The IEWT is used to decompose the current, and the modulus maximum of detail component c3 is calculated to construct the fault detection criterion. A weighted self-learning network is designed, according to the importance of the data to the classification task, different weights are allocated and nested in the front of the multi-view deep matrix factorization model. The fault features are extracted from the current component c1, c2, and c3, and the inter electrode voltage udc by using the IMDMF, and the fault classification is realized by the soft distribution layer. The results of simulation test show that the proposed fault detection scheme can meet the requirements of speed and reliability for fault detection, and the fault classification accuracy is high, which lays a good foundation for subsequent fault processing. |
| Key words: DC distribution network fault detection and classification improved empirical wavelet transform improved multi-view deep matrix factorization |
