Condition monitoring and fault diagnosis of wind turbines based on structural break detection in SCADA data

2021 ◽  
Author(s):  
Phong Ba Dao
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Structural Break

scholarly journals A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2801 ◽  
Author(s):  
Pinjia Zhang ◽  
Delong Lu
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Information Flow ◽  
Wind Turbines ◽  
Energy Flow ◽  
Global Climate ◽  
Rapid Development ◽  
Wind Farms ◽  
Flow Information ◽  
Comprehensive Survey

Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

scholarly journals Condition monitoring and vibration analysis of wind turbine

2020 ◽  
Vol 12 (3) ◽  
pp. 168781402091378 ◽  
Author(s):  
Feng Xiao ◽  
Chen Tian ◽  
Isaac Wait ◽  
Zhaohui (Joey) Yang ◽  
Benjamin Still ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Mode Coupling ◽  
Health Condition ◽  
Wind Turbine System ◽  
Spectral Models ◽  
Non Gaussian ◽  
Stochastic Properties

Health condition monitoring through comprehensive monitoring, incipient fault diagnosis, and the prediction of impending faults allows for the promotion of the long-term performance of wind turbines, particularly those in harsh environments such as cold regions. The condition monitoring of wind turbines is characterized by the difficulties associated with the lack of measured data and the nonstationary, stochastic, and complicated nature of vibration responses. This article presents a characterization of the vibrations of an operational wind turbine by spectrogram, scalogram, and bi-spectrum analyses. The results reveal varied nonstationary stochastic properties and mode-coupling instability in the vibrations of the tested wind turbine tower. The analysis illustrates that the wind turbine system vibrations exhibit certain non-Gaussian stochastic properties. An analytical model is used to evaluate the nonstationary, stochastic phenomena and mode-coupling phenomena observed in the experimental results. These results are of significance for the fault diagnosis of wind turbine system in operation as well as for improving fatigue designs beyond the wind turbulence spectral models recommended in the standards.

Predictive Condition Monitoring and Fault Diagnosis Techniques for Offshore Wind Turbines

2012 ◽  
Vol 608-609 ◽  
pp. 638-643
Author(s):  
Xiao Xia Zheng ◽  
Cong Jie Ye ◽  
Yang Fu ◽  
Dong Dong Li
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Offshore Wind ◽  
Special Focus ◽  
Offshore Wind Turbines ◽  
Wind Energy Conversion ◽  
Bearing Failures ◽  
Paper Review ◽  
Oil Monitoring

Wind energy conversion is the fastest-growing source of new electric generation in the world. The unforeseen damages of a component in a wind turbine have significant impact on the wind turbines, especially offshore ones. There has been a trend towards adoption of various forms of condition monitoring and fault diagnosis to know the deteriorating condition for both mechanical and electrial components, well in advance of a breakdown. This paper review the most recent advances in these areas with a special focus on offshore wind turbines. Different type of faults on offshore wind turbines are reviewed, including gearbox and bearing failures, generators failures, rotor failures and electrical failures. And then two widely-used methods(vibration analysis and oil monitoring) for predictive condition monitoring and fault diagnosis in offshore wind turbines are reviewed.

scholarly journals Fault diagnosis and condition monitoring of wind turbines

2017 ◽  
Vol 32 (4) ◽  
pp. 586-613 ◽  
Author(s):  
Henrik Niemann ◽  
Niels Kjølstad Poulsen ◽  
Mahmood Mirzaei ◽  
Lars Christian Henriksen
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wind Turbines

The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review

2015 ◽  
Vol 44 ◽  
pp. 466-472 ◽  
Author(s):  
W.Y. Liu ◽  
B.P. Tang ◽  
J.G. Han ◽  
X.N. Lu ◽  
N.N. Hu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wind Turbines

scholarly journals A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3236
Author(s):  
Phong B. Dao
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Multiple Linear Regression Model ◽  
Normal Operation ◽  
On Line ◽  
Using Data ◽  
The Stability ◽  
Main Ideas ◽  
Cusum Tests

This paper presents a cumulative sum (CUSUM)-based approach for condition monitoring and fault diagnosis of wind turbines (WTs) using SCADA data. The main ideas are to first form a multiple linear regression model using data collected in normal operation state, then monitor the stability of regression coefficients of the model on new observations, and detect a structural change in the form of coefficient instability using CUSUM tests. The method is applied for on-line condition monitoring of a WT using temperature-related SCADA data. A sequence of CUSUM test statistics is used as a damage-sensitive feature in a control chart scheme. If the sequence crosses either upper or lower critical line after some recursive regression iterations, then it indicates the occurrence of a fault in the WT. The method is validated using two case studies with known faults. The results show that the method can effectively monitor the WT and reliably detect abnormal problems.

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