Wind Turbine Pitch System Condition Monitoring and Fault Detection Based on Optimized Relevance Vector Machine Regression

2020 ◽  
Vol 11 (4) ◽  
pp. 2326-2336
Author(s):  
Lu Wei ◽  
Zheng Qian ◽  
Hamidreza Zareipour
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Relevance Vector Machine ◽  
Pitch System

scholarly journals Fault Detection and Diagnosis Methods for Fluid Power Pitch System Components—A Review

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1305
Author(s):  
Magnus F. Asmussen ◽  
Jesper Liniger ◽  
Henrik C. Pedersen
Keyword(s):  
Fault Detection ◽  
Power Systems ◽  
Wind Energy ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Fault Detection And Diagnosis ◽  
Fluid Power ◽  
Pitch System ◽  
Fluid Power Systems

Wind turbines have become a significant part of the global power production and are still increasing in capacity. Pitch systems are an important part of modern wind turbines where they are used to apply aerodynamic braking for power regulation and emergency shutdowns. Studies have shown that the pitch system is responsible for up to 20% of the total down time of a wind turbine. Reducing the down time is an important factor for decreasing the total cost of energy of wind energy in order to make wind energy more competitive. Due to this, attention has come to condition monitoring and fault detection of such systems as an attempt to increase the reliability and availability, hereby the reducing the turbine downtime. Some methods for fault detection and condition monitoring of fluid power systems do exists, though not many are used in today’s pitch systems. This paper gives an overview of fault detection and condition monitoring methods of fluid power systems similar to fluid power pitch systems in wind turbines and discuss their applicability in relation to pitch systems. The purpose is to give an overview of which methods that exist and to find areas where new methods need to be developed or existing need to be modified. The paper goes through the most important components of a pitch system and discuss the existing methods related to each type of component. Furthermore, it is considered if existing methods can be used for fluid power pitch systems for wind turbine.

scholarly journals Fault Detection of the Wind Turbine Variable Pitch System Based on Large Margin Distribution Machine Optimized by the State Transition Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mingzhu Tang ◽  
Jiahao Hu ◽  
Zijie Kuang ◽  
Huawei Wu ◽  
Qi Zhao ◽  
...  
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
State Transition ◽  
The State ◽  
Variable Pitch ◽  
Large Margin ◽  
Detection Model ◽  
Margin Distribution ◽  
State Transition Algorithm ◽  
Pitch System

Aiming at solving the problem that the parameters of a fault detection model are difficult to be optimized, the paper proposes the fault detection of the wind turbine variable pitch system based on large margin distribution machine (LDM) which is optimized by the state transition algorithm (STA). By setting the three parameters of the LDM model as a three-dimensional vector which was searched by STA, by using the accuracy of fault detection model as the fitness function of STA, and by adopting the four state transformation operators of STA to carry out global search in the form of point, line, surface, and sphere in the search space, the global optimal parameters of LDM fault detection model are obtained and used to train the model. Compared with the grid search (GS) method, particle swarm optimization (PSO) algorithm, and genetic algorithm (GA), the proposed model method has lower false positive rate (FPR) and false negative rate (FNR) in the fault detection of wind turbine variable pitch system in a real wind farm.

scholarly journals Fault Detection in the Blade and Pitch System of a Wind Turbine withH2PI Observers

2015 ◽  
Vol 659 ◽  
pp. 012033 ◽  
Author(s):  
Ester Sales-Setién ◽  
Ignacio Peñarrocha ◽  
Daniel Dolz ◽  
Roberto Sanchis
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Pitch System

scholarly journals Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6601
Author(s):  
Conor McKinnon ◽  
James Carroll ◽  
Alasdair McDonald ◽  
Sofia Koukoura ◽  
Charlie Plumley
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Supervisory Control ◽  
Data Acquisition System ◽  
Novel Technique ◽  
Machine Learning Model ◽  
System Data ◽  
Pitch System ◽  
Run Up ◽  
Isolation Forest

Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

scholarly journals Fault Detection of Wind Turbine Pitch System Based on Multiclass Optimal Margin Distribution Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Mingzhu Tang ◽  
Zijie Kuang ◽  
Qi Zhao ◽  
Huawei Wu ◽  
Xu Yang
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Power Output ◽  
Turbine Generator ◽  
Generator System ◽  
Operating Data ◽  
Wind Turbine Generator ◽  
Margin Distribution ◽  
Pitch System ◽  
Wind Turbine Generator System

In response to the unbalanced sample categories and complex sample distribution of the operating data of the pitch system of the wind turbine generator system, this paper proposes a method for fault detection of the pitch system of the wind turbine generator system based on the multiclass optimal margin distribution machine. In this method, the power output of the wind turbine generator system is used as the main status parameter, and the operating data history of the wind turbine generator system in the wind power supervisory control and data acquisition (SCADA) system is subject to correlation analysis with the Pearson correlation coefficient, to eliminate the features that have low correlation with the power output status parameter. Secondary analysis is performed to the remaining features, thus reducing the number and complexity of samples. Datasets are divided into the training set for training of the multiclass optimal margin distribution machine fault detection model and test set for testing. Experimental verification was carried out with the operating data of one wind farm in China. Experimental results show that, compared with other support vector machines, the proposed method has higher fault detection accuracy and precision and lower false-negative rate and false-positive rate.

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