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{"title":"Flexible DC Grid Fault Detection Method Based on MTF-EfficientNetV2 Algorithm","authors":"Zhihui Zeng, Jiayin Li, Yanfang Wei, Yingying Zheng","doi":"10.1002/tee.70256","DOIUrl":null,"url":null,"abstract":"<p>Given the swift advancement of clean energy, flexible DC grid has become a research hotspot for future power grids. Existing DC line fault detection methods have problems such as low detection precision and vulnerability to resistance. For this reason, a fault detection method built on the upgraded EfficientNetV2 algorithm is proposed. Primarily, the fault transient voltage time-domain data are gathered. To enhance the variability of fault features, the data are transformed to a two-dimensional image by Markov variation field. Then, a dual-channel attention mechanism is used to shortlist and fuse the features with channel and spatial features, respectively. Finally, the fused features are fed into EfficientNetV2 for training. And the detection results are obtained by testing the model under different working conditions. The findings demonstrate the excellent detection accuracy of the approach. The average accuracy can reach 98.95%. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 9","pages":"1328-1336"},"PeriodicalIF":1.0000,"publicationDate":"2026-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEJ Transactions on Electrical and Electronic Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/tee.70256","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/2/9 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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Abstract
Given the swift advancement of clean energy, flexible DC grid has become a research hotspot for future power grids. Existing DC line fault detection methods have problems such as low detection precision and vulnerability to resistance. For this reason, a fault detection method built on the upgraded EfficientNetV2 algorithm is proposed. Primarily, the fault transient voltage time-domain data are gathered. To enhance the variability of fault features, the data are transformed to a two-dimensional image by Markov variation field. Then, a dual-channel attention mechanism is used to shortlist and fuse the features with channel and spatial features, respectively. Finally, the fused features are fed into EfficientNetV2 for training. And the detection results are obtained by testing the model under different working conditions. The findings demonstrate the excellent detection accuracy of the approach. The average accuracy can reach 98.95%. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
基于mtf - effentnetv2算法的柔性直流电网故障检测方法
随着清洁能源的迅速发展,柔性直流电网已成为未来电网的研究热点。现有的直流线路故障检测方法存在检测精度低、易受电阻影响等问题。为此,提出了一种基于升级后的effentnetv2算法的故障检测方法。首先采集故障暂态电压时域数据。为了增强故障特征的可变性,利用马尔可夫变分场将数据转换为二维图像。然后,利用双通道注意机制分别筛选和融合通道特征和空间特征。最后,将融合的特征输入到EfficientNetV2中进行训练。通过对模型在不同工况下的测试,得到了检测结果。结果表明,该方法具有良好的检测精度。平均准确率可达98.95%。©2026日本电气工程师学会和Wiley期刊有限责任公司。
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