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

Tang, Mingzhu; Hu, Jiahao; Kuang, Zijie; Wu, Huawei; Zhao, Qi; Peng, Shuhao

doi:10.1155/2020/9718345

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Its internal structure is complex. When it operates in extremely harsh environment, it is likely to cause its failure (Tang et al 2020a ). The fault of variable pitch system directly affects the power operation efficiency of wind turbine (Tang et al 2020b ).…”

Section: Vghho For Fault Diagnosis Of Wind Turbinementioning

confidence: 99%

A velocity-guided Harris hawks optimizer for function optimization and fault diagnosis of wind turbine

et al. 2022

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Harris hawks optimizer (HHO) is a relatively novel meta-heuristic approach that mimics the behavior of Harris hawk over the process of predating the rabbits. The simplicity and easy implementation of HHO have attracted extensive attention of many researchers. However, owing to its capability to balance between exploration and exploitation is weak, HHO suffers from low precision and premature convergence. To tackle these disadvantages, an improved HHO called VGHHO is proposed by embedding three modifications. Firstly, a novel modified position search equation in exploitation phase is designed by introducing velocity operator and inertia weight to guide the search process. Then, a nonlinear escaping energy parameter E based on cosine function is presented to achieve a good transition from exploration phase to exploitation phase. Thereafter, a refraction-opposition-based learning mechanism is introduced to generate the promising solutions and helps the swarm to flee from the local optimal solution. The performance of VGHHO is evaluated on 18 classic benchmarks, 30 latest benchmark tests from CEC2017, 21 benchmark feature selection problems, fault diagnosis problem of wind turbine and PV model parameter estimation problem, respectively. The simulation results indicate that VHHO has higher solution quality and faster convergence speed than basic HHO and some well-known algorithms in the literature on most of the benchmark and real-world problems.

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Section: Vghho For Fault Diagnosis Of Wind Turbinementioning

confidence: 99%

A velocity-guided Harris hawks optimizer for function optimization and fault diagnosis of wind turbine

et al. 2022

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“…(Zhang and Zhou, 2014;Tang et al, 2019).introduced margin mean and margin variance on the basis of SVM and proposed a large margin distributed machine (LDM), and this method has better classification performance than SVM. (Tang et al, 2020a) used LDM to detect the fault of WT's pitch system and optimized it with state transition algorithm (STA), which significantly improved the accuracy of fault detection.…”

Section: Support Vector Machinementioning

confidence: 99%

Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

Tang

Zhao

et al. 2021

Front. Energy Res.

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Wind turbines (WTs) generally comprise several complex and interconnected systems, such as hub, converter, gearbox, generator, yaw system, pitch system, hydraulic system control system,integration control system, and auxiliary system. Moreover, fault diagnosis plays an important role in ensuring WT safety. In the past decades, machine learning (ML) has showed a powerful capability in fault detection and diagnosis of WTs, thereby remarkably reducing equipment downtime and minimizing financial losses. This study provides a comprehensive review of recent studies on ML methods and techniques for WT fault diagnosis. These studies are classified as supervised, unsupervised, and semi-supervised learning methods. Existing state-of-the-art methods are analyzed and characteristics are discussed. Perspectives on challenges and further directions are also provided.

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“…The FD methods based on machine learning include the Artificial Neural Network [9,10], Random Forest [11][12][13], LightGBM [14], Deep Learning [15,16], and the Large Margin Distribution Machine [17,18]. J. H. Pan [9] designed a data-driven method based on a deep convolutional neural network (DCNN) for the gain error, position deviation, and the faults of sensors and actuators.…”

Section: Introductionmentioning

confidence: 99%

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

Tang

et al. 2021

Sensors

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

Cited by 4 publications

References 21 publications

A velocity-guided Harris hawks optimizer for function optimization and fault diagnosis of wind turbine

A velocity-guided Harris hawks optimizer for function optimization and fault diagnosis of wind turbine

Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

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

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