Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines

The different operating conditions of wind turbines pose great challenges for efficient and reliable fault detection. Therefore, a good analysis of wind turbine data is essential in assessing the state of the wind turbines, since the traditional threshold cannot provide a timely warning as it indicates that the malfunction has already occurred. This paper presents a new method for analyzing the actual data of the turbines, using aggregated model consisting of the neighborhood comparison method, K-means clustering and decision tree model to diagnose faults. The wind speed of the adjacent turbines is compared with each other, then other parameters of the same wind speed are also compared with each other. The purpose of comparison is that, the wind turbines which are similar in wind speed are similar in performance as well. This approach helps us to discover the abnormal data for turbine performance with in the normal operating range. The abnormal performance of any turbine destroys the similarity relationship between its data and the neighboring unit’s data. The main advantage of this approach is the possibility to detect the beginning of abnormal performance in real time, a case study using real SCADA data is used to validate this approach, which demonstrates its effectiveness and advantages.

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Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling

Energies ◽

10.3390/en12060984 ◽

2019 ◽

Vol 12 (6) ◽

pp. 984 ◽

Cited By ~ 10

Author(s):

Hong Wang ◽

Hongbin Wang ◽

Guoqian Jiang ◽

Jimeng li ◽

Yueling Wang

Keyword(s):

Fault Detection ◽

Wind Turbines ◽

Health Monitoring ◽

Deep Belief Network ◽

Operating Conditions ◽

Behavior Modeling ◽

Operation Condition ◽

Belief Network ◽

Operation Conditions ◽

Early Fault Detection

Health monitoring and early fault detection of wind turbines have attracted considerable attention due to the benefits of improving reliability and reducing the operation and maintenance costs of the turbine. However, dynamic and constantly changing operating conditions of wind turbines still pose great challenges to effective and reliable fault detection. Most existing health monitoring approaches mainly focus on one single operating condition, so these methods cannot assess the health status of turbines accurately, leading to unsatisfactory detection performance. To this end, this paper proposes a novel general health monitoring framework for wind turbines based on supervisory control and data acquisition (SCADA) data. A key feature of the proposed framework is that it first partitions the turbine operation into multiple sub-operation conditions by the clustering approach and then builds a normal turbine behavior model for each sub-operation condition. For normal behavior modeling, an optimized deep belief network is proposed. This optimized modeling method can capture the sophisticated nonlinear correlations among different monitoring variables, which is helpful to enhance the prediction performance. A case study of main bearing fault detection using real SCADA data is used to validate the proposed approach, which demonstrates its effectiveness and advantages.

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Cross-Turbine Training of Convolutional Neural Networks for SCADA-Based Fault Detection in Wind Turbines

Annual Conference of the PHM Society ◽

10.36001/phmconf.2020.v12i1.1205 ◽

2020 ◽

Vol 12 (1) ◽

pp. 10

Author(s):

Markus Ulmer ◽

Eskil Jarlskog ◽

Gianmarco Pizza ◽

Lilach Goren Huber

Keyword(s):

Neural Networks ◽

Fault Detection ◽

Convolutional Neural Networks ◽

Wind Turbines ◽

Practical Aspect ◽

Machine Learning Algorithms ◽

Training Scheme ◽

Early Fault Detection ◽

Training Schemes ◽

Energy Community

Machine learning algorithms for early fault detection of wind turbines using 10-minute SCADA data are attracting attention in the wind energy community due to their cost-effectiveness. It has been recently shown that convolutional neural networks (CNNs) can significantly improve the performance of such algorithms. One practical aspect in the deployment of these algorithms is that they require a large amount of historical SCADA data for training. These are not always available, for example in the case of newly installed turbines. Here we suggest a cross-turbine training scheme for CNNs: we train a CNN model on a turbine with abundant data and use the trained network to detect faults in a different wind turbine for which only little data are available. We show that this scheme is able to detect faults with an accuracy and robustness which are very similar to the single-turbine scheme, in which training and detection are both done on the same turbine. We demonstrate this for two different fault types: abrupt and slowly evolving faults and perform a sensitivity analysis in order to compare the performance of the two training schemes. We show that the scheme works successfully also when training on turbines from another farm and with different measured variables than the target turbine.

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