Effective fault diagnosis method for the pitch system, the drive train, and the generator with converter in a wind turbine system

2022 ◽  
Author(s):  
Abdelmoumen Saci ◽  
Lakhmissi Cherroun ◽  
Ahmed Hafaifa ◽  
Omar Mansour
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Drive Train ◽  
Wind Turbine System ◽  
Diagnosis Method ◽  
Pitch System

Multi-fault diagnosis method for wind power generation system based on recurrent neural network

2019 ◽  
Vol 233 (5) ◽  
pp. 604-615 ◽  
Author(s):  
Junnian Wang ◽  
Yao Dou ◽  
Zhenheng Wang ◽  
Dan Jiang
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Power ◽  
Recurrent Neural Network ◽  
Generation System ◽  
Wind Turbine System ◽  
Diagnosis Method ◽  
Power Generation System

With the continuous expansion of the scale of wind turbine system, wind power production, operation and equipment control of wind turbine have become more and more significant. To improve the reliability of wind turbine systems fault diagnosis, combining with data-driven technology, this paper proposes a multi-fault diagnosis method for wind power system based on recurrent neural network. According to the actual wind speed data, the normal operation and fault data of the wind turbine system are obtained by system modeling, and the classification and prediction model based on the recurrent neural network algorithm is established, which takes 30 characteristic parameters such as wind speed, rotor speed, generator speed and power generation as input, and 10 different types faults labels of the wind turbine as output. Specific rules formed inside the sample data of the wind turbine system are learned intelligently by the model which is continuously trained, optimized and tested to verify the feasibility of the algorithm. The results of evaluation standards such as accuracy rate, missed detection rate and F1-measure that compared with other related algorithms such as deep belief network show that the proposed algorithm can solve the problem of multi-classification fault diagnosis for wind power generation system efficiently.

Fault Diagnosis Method for Wind Turbine Gearbox Based on Image Characteristics Extraction and Actual Value Negative Selection Algorithm

2020 ◽  
Vol 34 (14) ◽  
pp. 2054034
Author(s):  
Xiaoli Xu ◽  
Xiuli Liu
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Negative Selection ◽  
Selection Algorithm ◽  
Turbine Generator ◽  
Time Frequency ◽  
Negative Selection Algorithm ◽  
Wind Turbine Generator ◽  
Image Characteristics ◽  
Diagnosis Method

With the development of information theory and image analysis theory, the studies on fault diagnosis methods based on image processing have become a hot spot in the recent years in the field of fault diagnosis. The gearbox of wind turbine generator is a fault-prone subassembly. Its time frequency of vibration signals contains abundant status information, so this paper proposes a fault diagnosis method based on time-frequency image characteristic extraction and artificial immune algorithm. Firstly, obtain the time-frequency image using wavelet transform based on threshold denoising. Secondly, acquire time-frequency image characteristics by means of Hu invariant moment and correlation fusion gray-level co-occurrence matrix of characteristic value, thus, to extract the fault information of the gearing of wind turbine generator. Lastly, diagnose the fault type using the improved actual-value negative selection algorithm. The application of this method in the gear fault diagnosis on the test bed of wind turbine step-up gearbox proves that it is effective in the improvement of diagnosis accuracy.

A novel wind turbine bearing fault diagnosis method based on Integral Extension LMD

Measurement ◽  
2015 ◽  
Vol 74 ◽  
pp. 70-77 ◽  
Author(s):  
W.Y. Liu ◽  
Q.W. Gao ◽  
G. Ye ◽  
R. Ma ◽  
X.N. Lu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Integral Extension ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Turbine Bearing

scholarly journals Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

2019 ◽  
Vol 9 (4) ◽  
pp. 783 ◽  
Author(s):  
Silvio Simani ◽  
Paolo Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Offshore Wind ◽  
Data Driven ◽  
Offshore Wind Turbine ◽  
Wind Turbine System ◽  
High Degree ◽  
Offshore Plants

Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.

scholarly journals Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shoubin Wang ◽  
Xiaogang Sun ◽  
Chengwei Li
Keyword(s):  
Time Series ◽  
Riemannian Manifold ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Covariance Matrix ◽  
Multivariate Time Series ◽  
Time Sequence ◽  
Mechanical Equipment ◽  
Wind Turbine Gearbox ◽  
Diagnosis Method

As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance matrix as a descriptor of the time sequence. Riemannian distance is used as the similarity measure and the statistical process control diagram is applied to detect the abnormity of multivariate time series. And the visualization of the covariance matrix distribution is used to detect the abnormity of mechanical equipment, leading to realize the fault diagnosis. With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective.

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