A New Condition Monitoring Method for Wind Turbines Based on Power Curve Model

2016 ◽  
Author(s):  
Jianlou Lou ◽  
Kai Shan ◽  
Jia Xu
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Power Curve ◽  
Monitoring Method ◽  
Curve Model

Development of a Novel Power Curve Monitoring Method for Wind Turbines and Its Field Tests

2014 ◽  
Vol 29 (1) ◽  
pp. 119-128 ◽  
Author(s):  
Joon-Young Park ◽  
Jae-Kyung Lee ◽  
Ki-Yong Oh ◽  
Jun-Shin Lee
Keyword(s):  
Wind Turbines ◽  
Field Tests ◽  
Power Curve ◽  
Monitoring Method

scholarly journals Condition monitoring of wind turbines using machine learning based anomaly detection and statistical techniques for the extraction of 'healthy data'

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xavier Chesterman ◽  
Timothy Verstraeten ◽  
Pieter-Jan Daems ◽  
Jan Helsen
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Operating Conditions ◽  
Training Data ◽  
Series Data ◽  
Statistical Techniques ◽  
Monitoring Method ◽  
One Year

Premature failures caused by excessive wear are responsible for a large fraction of the maintenance costs of wind turbines. Therefore, it is crucial to be able to identify the propagation of these failures as early as possible. To this end, a novel condition monitoring method is proposed that uses statistical data analysis techniques and machine learning to construct a multivariate anomaly detection framework, based on high-frequency temperature SCADA data from wind turbines. This framework contains several steps. First, there is a preprocessing step in which relevant wind turbine states are extracted from the data. These states are the operating conditions and whether or not the turbine exhibits transient behavior. The second step entails anomaly detection on the temperature time series data. Fleet information is used to filter out exogenous (environmental) factors. Furthermore, multiple models are combined to get more stable and robust anomaly detections. By combining them the weaknesses of the individual models are alleviated resulting in a better overall performance. A limitation of machine learning-based anomaly detection on temperature data is the requirement that at least one year of “healthy” (meaning without anomalies) training data is available to account for seasonal effects. The lack of verified “healthy” data spread out evenly over the seasons generally means that the anomaly detection accuracy is severely compromised for the unrepresented seasons. This research uses smart retraining to reduce this limitation. Statistical techniques that leverage the information of the fleet are used to extract “healthy” data from at least one year, but preferably multiple years, of unverified data. This can then be used as training data for the machine learning-based models. To validate the pipeline, temperature and failure data of a real operational wind farm is used. Although the methodology is general in its scope, the validation case focusses specifically on generator bearing failures.

scholarly journals A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3411 ◽  
Author(s):  
Peng Qian ◽  
Xiange Tian ◽  
Jamil Kanfoud ◽  
Joash Lee ◽  
Tat-Hean Gan
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Defect Detection ◽  
Short Term Memory ◽  
Wind Farms ◽  
Economic Benefits ◽  
Short Term ◽  
Monitoring Method ◽  
Term Memory ◽  
Long Short Term Memory

Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

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.

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