Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning
Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning
复制标题
使用监控和数据采集数据以及集成深度学习进行智能风力涡轮机叶片结冰检测
DOI：
10.1002/ese3.449
复制
发表时间：
2019-10-24
影响因子：
3.8
通讯作者：
Cui, Huan
中科院分区：
文献类型：
--
作者：
Liu, Yao;Cheng, Han;Cui, Huan
风机叶片结冰是影响风机运行安全和发电效率的主要故障之一。目前的结冰检测方法主要是基于气象观测系统或附加状态监测系统。与现有方法相比，利用风力发电机组SCADA数据进行结冰检测具有成本低、稳定性高、早期发现结冰能力强等诸多潜在优势。然而，目前在这方面还没有深入的研究。提出了一种基于风力机SCADA数据的风力机叶片结冰智能检测方法。该方法包括SCADA数据预处理、自动特征提取和集成结冰检测模型构建三个过程。具体而言，采用深度自编码器网络自适应地从复杂的SCADA数据中学习多级故障特征。利用集成技术，充分利用深度自编码器网络不同隐藏层提取的所有特征，构建集成结冰检测模型。利用实际风电场的数据验证了该方法的有效性。实验结果表明，该方法不仅能够自适应地从复杂的SCADA数据中提取有价值的故障特征，而且与传统的机器学习模型和单个深度学习模型相比，具有更高的检测精度和泛化能力。
Ice accretion on wind turbine blades is one of the major faults affecting the operational safety and power generation efficiency of wind turbines. Current icing detection methods are based on either meteorological observing system or extra condition monitoring system. Compared with current methods, icing detection using the intrinsic supervisory control and data acquisition (SCADA) data of wind turbines has plenty of potential advantages, such as low cost, high stability, and early icing detection ability. However, there have not been deep investigations in this field at present. In this paper, a novel intelligent wind turbine blade icing detection method based on the wind turbine SCADA data is proposed. This method consists of three processes: SCADA data preprocessing, automatic feature extraction, and ensemble icing detection model construction. Specifically, deep autoencoders network is employed to learn multilevel fault features from the complex SCADA data adaptively. And the ensemble technique is utilized to make full use of all the extracted features from different hidden layers of the deep autoencoders network to build the ensemble icing detection model. The effectiveness of the proposed method is validated using the data collected from actual wind farms. The experimental results reveal that the proposed method is able to not only adaptively extract valuable fault features from the complex SCADA data, but also obtains higher detection accuracy and generalization capability compared with conventional machine learning models and individual deep learning model.