With the increasing installation of the wind turbines both onshore and offshore, condition monitoring technologies and systems have become increasingly important in order to reduce the downtime and operations and maintenance (O&M) cost, thus maximising economic benefits. This paper presents a novel machine learning model-based data-driven approach to accurately evaluate the performance of the turbines and diagnose the faults. The approach is based on Long-short term memory (LSTM) incorporating a statistical tool named Kullback-Leibler divergence (KLD). The hybrid LSTM-KLD method has been applied to two faulty wind turbines with gearbox bearing fault and generator winding fault respectively for fault detection and identification. The proposed method is then compared with three other well-established machine-learning algorithms to investigate its superiority. The results show that the proposed method can produce a more effective detection with accuracy reaching 94% and 92% for the turbines, respectively. Furthermore, the proposed method can effectively distinguish the alarms from the faults, from which the distinguished alarms can be considered as an early warning of the fault occurrence.

随着陆上和海上风力涡轮机的安装越来越多，状态监测技术和系统变得越来越重要，以减少停机时间和运营和维护 (O&M) 成本，从而最大限度地提高经济效益。本文提出了一种新的基于机器学习模型的数据驱动方法，以准确评估涡轮机的性能并诊断故障。该方法基于长短期记忆 (LSTM)，并结合了名为 Kullback-Leibler 分歧 (KLD) 的统计工具。将混合 LSTM-KLD 方法应用于分别具有齿轮箱轴承故障和发电机绕组故障的两台故障风电机组进行故障检测和识别。然后将所提出的方法与其他三种成熟的机器学习算法进行比较，以研究其优越性。结果表明，所提出的方法可以产生更有效的检测，对涡轮机的准确率分别达到 94% 和 92%。此外，所提出的方法可以有效地将告警与故障区分开来，从中区分出的告警可以看作是对故障发生的早期预警。