Zhang Lei, Xu Zhongyang, Lu Chen, Qiao Tianjiao, Su Hongzhi, Luo Yazhou
North China Branch of State Grid Corporation of China, China.
Heliyon. 2024 May 4;10(9):e30670. doi: 10.1016/j.heliyon.2024.e30670. eCollection 2024 May 15.
Establishing a deep learning model for transformer fault diagnosis using transformer oil chromatogram data requires a large number of fault samples. The lack and imbalance of oil chromatogram data can lead to overfitting, lack of representativeness of the model, and unsatisfactory prediction results on test set data, making it difficult to accurately diagnose transformer faults. A conditional Wasserstein generative adversarial network with gradient penalty optimization (CWGAN-GP) is adopted in this paper, which based on gradient penalty optimization and expand the oil chromatography fault samples of 500 sets of transformer oil chromatography data with 5 types of faults. The proposed method is used to classify transformer faults using a deep autoencoder, and the sample quality of the neural network model proposed in this paper is compared with several other variants of generative adversarial neural network models. The research results show that after using the method proposed in this paper for sample expansion, the overall accuracy of fault diagnosis can reach 93.2 %, which is 4.98 % higher than the original imbalanced samples. Compared with other sample expansion methods, the accuracy of fault diagnosis of the algorithm in this paper is improved by 1.70 %-3.05 %.
利用变压器油色谱数据建立用于变压器故障诊断的深度学习模型需要大量的故障样本。油色谱数据的缺乏和不平衡会导致过拟合、模型缺乏代表性以及对测试集数据的预测结果不理想，从而难以准确诊断变压器故障。本文采用了一种带梯度惩罚优化的条件瓦瑟斯坦生成对抗网络（CWGAN-GP），该网络基于梯度惩罚优化，对500组具有5种故障类型的变压器油色谱数据的油色谱故障样本进行了扩充。所提方法用于通过深度自动编码器对变压器故障进行分类，并将本文提出的神经网络模型的样本质量与生成对抗神经网络模型的其他几种变体进行了比较。研究结果表明，使用本文提出的方法进行样本扩充后，故障诊断的总体准确率可达93.2%，比原始不平衡样本高4.98%。与其他样本扩充方法相比，本文算法的故障诊断准确率提高了1.70%-3.05%。
