Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network

Yaguang LI; Meng LI

doi:10.12096/j.2096-4528.pgt.20091

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Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network

New and Renewable Energy | 更新时间：2025-07-04

- Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network
- Power Generation Technology Vol. 42, Issue 3, Pages: 313-321(2021)
- 作者机构：
  
  1.华电煤业集团有限公司, 北京市西城区 100035
  2.北京交通大学机械与电子控制工程学院, 北京市海淀区 100044
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(50776005);National Natural Science Foundation of China(51577008)
- DOI：10.12096/j.2096-4528.pgt.20091
  CLC： TK83
- Received：19 September 2020，
  
  Published：30 June 2021
- 稿件说明：
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Yaguang LI, Meng LI. Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network[J]. Power Generation Technology, 2021, 42(3): 313-321.
DOI：

Yaguang LI, Meng LI. Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network[J]. Power Generation Technology, 2021, 42(3): 313-321. DOI： 10.12096/j.2096-4528.pgt.20091.

摘要

准确有效的状态监测是提高风电机组可靠性和安全性的关键。近年来，基于深度神经网络(deep neural network，DNN)的智能化异常检测方法越来越受到人们的重视。针对实际工业中难以获得准确的有标签数据的问题，提出了一种基于无监督学习的深度小世界神经网络(deep small-world neural network，DSWNN)来检测风电机组的早期故障。在深度置信网络(deep belief network，DBN)构建过程中，首先采用多个受限玻尔兹曼机(restricted Boltzmann machines，RBM)堆叠常规自动编码网络，并利用风机的无标签数据采集与监视控制(supervisory control and data acquisition，SCADA)数据进行预训练。然后，利用随机加边法将训练后的网络进行小世界特性转换，再利用最少的有标签数据对网络参数进行微调训练。此外，为了应付风速扰动并减少虚警，又提出了一种基于极值理论的自适应阈值作为异常判断准则。最后，通过2个风机异常检测的应用实例，并与DBN和DNN算法进行了对比，验证了该方法具有良好的有效性和准确性。

Abstract

Accurate and efficient condition monitoring is the key to enhance the reliability and security of wind turbines. In recent years

the intelligent anomaly detection method based on deep neural networks (DNN) has been receiving increasing attention. Since accurately labeled data are usually difficult to obtain in real industries

this paper proposed a novel deep small-world neural network (DSWNN) on the basis of unsupervised learning to detect the early failure of wind turbines. During the deep belief network (DBN) construction

a regular auto-encoder network with multiple restricted Boltzmann machines (RBM) was first stacked and pre-trained by using unlabeled supervisory control and data acquisition (SCADA) data of wind turbines. After that

the trained network was transformed into a DSWNN model by the randomly add-edges method

where the network parameters are fine-tuned by using minimal amounts of labeled data. In order to deal with the disturbances of wind speed and reduce false alarms

an adaptive threshold based on extreme value theory was presented as the criterion of anomaly judgment. The DSWNN model is excellent in depth mining data characteristics and accurate measurements. Finally

two failure cases of wind turbine anomaly detection were given to demonstrate the validity and accuracy of the proposed DSWNN contrasted with the DBN and DNN algorithms.

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