基于经验小波变换和 WOA-CNN 的柔性直流电网故障检测

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Electrical Engineering & Technology Pub Date : 2024-09-14 DOI:10.1007/s42835-024-02038-9
Yan-Fang Wei, Ping Yang, Zhan-Ye Yang, Peng Wang, Xiao-Wei Wang
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引用次数: 0

摘要

柔性直流电网解决了传统交流电网线损高、输电容量小的缺点,但仍存在特征信号提取难、故障诊断难等问题。为解决这一问题,本文提出了一种基于经验小波变换(EWT)与多尺度模糊熵(MFE)和鲸鱼算法优化与卷积神经网络(WOA-CNN)的故障检测方法。首先,利用 EWT 对故障线路模式电压信号进行分解,得到故障分量。然后,计算各分量的相关系数,重建特征信息较多的分量。计算重建信号在不同故障下的 MFE 值。最后,将故障特征量输入 WOA-CNN 进行分类。大量实验证明,该方法具有较强的抗干扰能力和较高的准确度,能在不同故障类型、故障位置和过渡电阻条件下可靠地检测线路故障。与 CNN、PSO-CNN、K-means 聚类、PSO-SVM 和 BP 神经网络相比,其准确率明显提高,平均达到 99.5834%。
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Fault Detection of Flexible DC Grid Based on Empirical Wavelet Transform and WOA-CNN

Flexible DC grid solves the disadvantages of high line loss and small transmission capacity of traditional AC grid, but it still has the problems of difficult to extract characteristic signals and fault diagnosis. To solve this problem, a fault detection method based on empirical wavelet transform (EWT) with multiscale fuzzy entropy (MFE) and Whale algorithm optimization with convolutional neural network (WOA-CNN) is proposed. Firstly, EWT is used to decompose the fault line mode voltage signal and obtain the fault component. Then, the correlation coefficient of each component is calculated, and the components with more feature information are reconstructed. The MFE value of the reconstructed signal under different faults is calculated. Finally, the fault feature quantity is input into WOA-CNN for classification. A large number of experiments demonstrate that this method has strong anti-interference ability and high accuracy, and can reliably detect line fault under different fault types, fault positions and transition resistance conditions. Its accuracy is significantly improved comparing with CNN, PSO-CNN, K-means clustering, PSO-SVM and BP neural network, with an average of 99.5834%.

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来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
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