To reduce the operation & maintenance cost of the wind turbine and improve its reliability, we propose a novel combined method for real-time operational state prediction, based on the long short-term memory and fuzzy synthetic assessment. After analyzing and filtering the monitoring data of a 2-MW wind turbine gearbox (WTG), we propose a deep learning-based multi-index operational state assessment framework. Following this, the prediction dimensions of each assessment index are established based on the correlation analysis. Meanwhile, we have obtained each index's weight and membership degree after analyzing the prediction error based on Long Short-Term Memory (LSTM). Case studies are performed using three-month Supervisory Control and Data Acquisition (SCADA) data of a 2-MW WTG with fault information. The results demonstrate that the difference between normal and fault state is more prominent when the prediction dimensions with lower correlation are selected. The degree of fault reflected by different assessment indexes is distinguished even under the same state. Then, through reviewing the alarm history of the condition monitoring system, we find that the proposed method can be used to detect the potential failures of the WTG.

为了降低风电机组的运行维护成本，提高其可靠性，我们提出了一种基于长短期记忆和模糊综合评估的实时运行状态预测组合方法。在对 2 MW 风力涡轮机齿轮箱 (WTG) 的监测数据进行分析和过滤后，我们提出了一种基于深度学习的多指标运行状态评估框架。在此之后，基于相关性分析建立了每个评估指标的预测维度。同时，我们在分析了基于长短期记忆（LSTM）的预测误差后，得到了每个指标的权重和隶属度。案例研究是使用带有故障信息的 2 兆瓦风力发电机组的三个月监控和数据采集 (SCADA) 数据进行的。The results demonstrate that the difference between normal and fault state is more prominent when the prediction dimensions with lower correlation are selected. 即使在同一状态下，不同评价指标所反映的故障程度也是有区别的。然后，通过回顾状态监测系统的报警历史，我们发现所提出的方法可用于检测风力发电机组的潜在故障。