Data annotation and feature extraction in fault detection in a wind turbine hydraulic 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.

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

Journal of Physics Conference Series ◽

10.1088/1742-6596/659/1/012033 ◽

2015 ◽

Vol 659 ◽

pp. 012033 ◽

Cited By ~ 1

Author(s):

Ester Sales-Setién ◽

Ignacio Peñarrocha ◽

Daniel Dolz ◽

Roberto Sanchis

Keyword(s):

Fault Detection ◽

Wind Turbine ◽

Pitch System

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

Mathematical Problems in Engineering ◽

10.1155/2020/2091382 ◽

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

Energies ◽

10.3390/en14051305 ◽

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.

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Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF

Sensors ◽

10.3390/s21186215 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6215

Author(s):

Mingzhu Tang ◽

Jiabiao Yi ◽

Huawei Wu ◽

Zimin Wang

Keyword(s):

Random Forest ◽

Fault Detection ◽

Wind Turbine ◽

Fault Model ◽

Training Set ◽

Turbine Generator ◽

Data Set ◽

Test Set ◽

Wind Turbine Generator ◽

Pitch System

It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set.

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