With the increasing scale and number of wind farms, wind turbines' daily operation and maintenance costs are increasing. To reduce operation and maintenance costs and enhance the reliability of wind turbine and system operation data before reaching catastrophic failures, monitoring the operating status of the equipment and detecting failures at an early stage is crucial. It is of great practical significance to utilize the working condition data for abnormal assessment of the operating status of wind turbines to realize abnormal monitoring of the operating status of wind turbines. However, the existing anomaly detection methods can neither perform effective relational modeling in data filled with a large amount of redundant information nor reasonably utilize the valuable anomaly data. For this reason, this paper proposes an anomaly detection model that fuses a Transformer and a generative adversarial network. Firstly, it reduces the leakage detection rate of minor deviation anomalies by amplifying the reconstruction error. Secondly, it uses autoregressive inference to extract multimodal features to enhance the stability and generalization ability of training. Finally, the temporal feature extraction module is constructed to promote the interactive learning between features of different time scales and effectively reduce the time redundancy. The results of multiple sets of experiments conducted on real WTG datasets show that TransGAN-WT achieves an average F1 score of 96.10% across multiple wind turbine datasets, which is 5.84% and 2.89% higher than several other state-of-the-art baseline methods. It also realizes a false positive rate (FPR) of 0.06%, and is verified by the Wilcoxon signed-rank test to have achieved a statistically significant performance enhancement compared to the state-of-the-art baseline methods, effectively ensuring the stable operation of wind turbines.

翻译：随着风电场规模和数量的不断增加，风电机组的日常运行与维护成本日益攀升。为降低运维成本并提升风电机组在达到灾难性故障前的运行数据可靠性，对设备运行状态进行监测并及早发现故障至关重要。利用工况数据对风电机组运行状态进行异常评估，实现其运行异常监测具有重要的现实意义。然而，现有异常检测方法既无法在充斥着大量冗余信息的数据中进行有效的关系建模，也无法合理利用宝贵的异常数据。为此，本文提出一种融合Transformer与生成对抗网络的异常检测模型。首先，通过放大重构误差降低轻微偏差异常的漏检率；其次，利用自回归推理提取多模态特征，以增强训练的稳定性与泛化能力；最后，构建时序特征提取模块，促进不同时间尺度特征间的交互学习，并有效降低时间冗余度。在真实风电机组数据集上进行的多组实验结果表明，TransGAN-WT在多个风电机组数据集上的平均F1分数达到96.10%，相比其他几种最先进基线方法分别高出5.84%和2.89%。同时，该模型实现了0.06%的假正率，并且通过Wilcoxon符号秩检验验证，其相比最先进基线方法取得了统计上显著的性能提升，有效保障了风电机组的稳定运行。