scholarly journals Combining SCADA and vibration data into a single anomaly detection model to predict wind turbine component failure

Wind Energy ◽  
2020 ◽  
Author(s):  
Alan Turnbull ◽  
James Carroll ◽  
Alasdair McDonald
Keyword(s):  
Anomaly Detection ◽  
Wind Turbine ◽  
Component Failure ◽  
Detection Model ◽  
Vibration Data ◽  
Turbine Component

scholarly journals Wind Turbine Anomaly Detection Based on SCADA Data Mining

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 751
Author(s):  
Xiaoyuan Liu ◽  
Senxiang Lu ◽  
Yan Ren ◽  
Zhenning Wu
Keyword(s):  
Anomaly Detection ◽  
Wind Turbine ◽  
Supervisory Control ◽  
Detection Method ◽  
Classification Result ◽  
Detection Model ◽  
Scada System ◽  
Silhouette Coefficient ◽  
Abnormal State ◽  
Low Dimensional

In this paper, a wind turbine anomaly detection method based on a generalized feature extraction is proposed. Firstly, wind turbine (WT) attributes collected from the Supervisory Control And Data Acquisition (SCADA) system are clustered with k-means, and the Silhouette Coefficient (SC) is adopted to judge the effectiveness of clustering. Correlation between attributes within a class becomes larger, correlation between classes becomes smaller by clustering. Then, dimensions of attributes within classes are reduced based on t-Distributed-Stochastic Neighbor Embedding (t-SNE) so that the low-dimensional attributes can be more full and more concise in reflecting the WT attributes. Finally, the detection model is trained and the normal or abnormal state is detected by the classification result 0 or 1 respectively. Experiments consists of three cases with SCADA data demonstrate the effectiveness of the proposed method.

scholarly journals Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 982 ◽  
Author(s):  
Xin Wu ◽  
Hong Wang ◽  
Guoqian Jiang ◽  
Ping Xie ◽  
Xiaoli Li
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Operating Conditions ◽  
Dynamic Threshold ◽  
Detection Accuracy ◽  
Echo State Network ◽  
Monitoring Method ◽  
Vibration Data ◽  
Static Monitoring ◽  
Threshold Monitoring

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

scholarly journals Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3333
Author(s):  
Maria del Cisne Feijóo ◽  
Yovana Zambrano ◽  
Yolanda Vidal ◽  
Christian Tutivén
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Environmental Conditions ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Small Subset ◽  
Scaled Model ◽  
Vibration Data ◽  
Large Water

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

scholarly journals An Efficient Distributed Anomaly Detection Model for Wireless Sensor Networks

2013 ◽  
Vol 5 ◽  
pp. 9-14 ◽  
Author(s):  
Murad A. Rassam ◽  
Anazida Zainal ◽  
Mohd Aizaini Maarof
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Wireless Sensor ◽  
Detection Model

scholarly journals Maintenance optimization for a single wind turbine component under time-varying costs

2021 ◽  
Author(s):  
Thijs Nicolaas Schouten ◽  
Rommert Dekker ◽  
Mustafa Hekimoğlu ◽  
Ayse Sena Eruguz
Keyword(s):  
Wind Turbine ◽  
Maintenance Optimization ◽  
Time Varying ◽  
Turbine Component

Investigation of anomaly detection technique for wind turbine pitch systems

2021 ◽  
Author(s):  
Conor McKinnon ◽  
James Carroll ◽  
Alasdair McDonald ◽  
Sofia Koukoura ◽  
Charlie Plumley
Keyword(s):  
Anomaly Detection ◽  
Wind Turbine ◽  
Detection Technique

Statistical Analysis of Component Failures: A 16 Year Survey on More Than 550 Wind Turbines

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Lorenzo Ferrari ◽  
Guido Soldi ◽  
Alessandro Bianchini ◽  
Enzo Dalpane
Keyword(s):  
Statistical Analysis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Good Prediction ◽  
Component Failure ◽  
Machine Age ◽  
Detailed Survey ◽  
Maintenance Program ◽  
Component Failures

A good prediction of the failure ratio of wind turbine (WT) components is pivotal to define a correct maintenance program and reduce the downtime periods. Even a small failure can lead to long downtime periods and high repairing costs. The installation sites, which generally have limited accessibility, and the necessity of special facilities to reach the components inside the nacelle, also play a key role in the correct management of WTs. In this study, a detailed survey on the failures occurred to the WTs managed by the Italian operator “e2i energie speciali” (more than 550 machines) over 16 years was performed and the results were analyzed in detail. Each failure was classified by considering the damaged component and the related downtime period. The analysis allowed the determination of several useful results such as the trend of failure occurrence with machine age and the identification of components and macrocomponents which are more critical in terms of both number of occurrences and downtime periods. The combination of component failure occurrences and related downtime periods was also computed to estimate which component is most critical for WT operation.

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