Journal of Electrical Engineering and Automation
Journal of Electrical Engineering and Automation. 2025; 4: (3) ; 10.12208/j.jeea.20250073 .
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南京邮电大学 江苏南京
*通讯作者： 吴祎,单位：南京邮电大学 江苏南京；
输电线路是电能传输的核心，对电力系统稳定性有重要影响。因此，当输电线路发生故障时，快速准确地识别出故障类型十分关键。在传统的故障诊断方法中，特征的选取过于依赖于主观判断，可能会遗漏重要特征信息。针对此问题，本研究提出一种基于RPM映射和GAN-CNN-RF的输电线路故障诊断方法，首先，采用RPM（Relative Position Matrix）将原始故障信号映射为二维图像。然后，使用CNN（Convolutional Neural Network）自动提取二维图像的典型特征并输入到RF（Random Forest）中实现故障诊断。针对真实故障数据难以获得的问题，本研究采用GAN（Generative Adversarial Network）对真实故障数据的训练集进行扩充，有效地提升了训练效果。采用Simulink模拟数据集和真实数据集两种数据集对模型进行了训练和测试。与EfficientNet，AlexNet，LeNet等模型相比，所提出的CNN-RF在两种数据集上的各项指标均为最优，验证了所提方法在输电线路故障诊断中的有效性。
Transmission lines are the core component of electrical energy transmission. Consequently, rapid and accurate identification of fault types is considered crucial when faults occur in transmission lines. For traditional fault diagnosis methods, feature selection is excessively dependent on subjective judgment, which could miss the critical feature information. A transmission line fault diagnosis method based on RPM mapping and GAN-CNN-RF was proposed in this research. First, original signals were transformed into two-dimensional images by RPM (Relative Position Matrix) mapping. Subsequently, typical features of the two-dimensional images were automatically extracted by a CNN (Convolutional Neural Network) and fed into a RF (Random Forest) for fault diagnosis. Considering the difficulty in obtaining the real fault data, the training set of real fault data was expanded by a GAN (Generative Adversarial Network), which effectively enhances the training effect. The model was validated on both Simulink-simulated datasets and real-world datasets. Compared with the models including EfficientNet, AlexNet, and LeNet, the proposed CNN-RF performed well across all evaluation metrics on both datasets. The effectiveness of this method in transmission line fault diagnosis was experimentally confirmed.
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