Fault Diagnosis Strategy for Wind Turbine Generator Based on the Gaussian Process Metamodel

Zhang, Dongmei; Yuan, Jun; Zhu, Jiang; Ji, Qingchang; Zhang, Xintong; Hao, Lei

doi:10.1155/2020/4295093

Cited by 4 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the complex relationship between the features and the outputs was initially obtained with the vibration signal as a parameter. Other researchers [39] constructed a wind farm fault prediction model by using a Gaussian process meta-model to determine the key factors affecting the performance of the system and proved the accuracy of the constructed model by comparing it with the actual observation results. Therefore, the factors affecting the key performance characteristic indexes of the system can be deciphered in subsequent studies.…”

Section: Previous Related Workmentioning

confidence: 99%

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

Gou,

Zhang,

Wen

et al. 2024

Sustainability

View full text Add to dashboard Cite

The use of renewable energy sources, such as wind power, has received more attention in China, and wind turbine system reliability has become more important. Based on existing research, this study proposes a state reliability prediction model for wind turbine systems based on XGBoost–LSTM. By considering the dynamic variability of the weight fused by the algorithm, under the irregular fluctuation of the same parameter with time in nonlinear systems, it reduces the algorithm defects in the prediction process. The improved algorithm is validated by arithmetic examples, and the results show that the root mean square error value (hereinafter abbreviated as RMSE) and the mean absolute error value (hereinafter abbreviated as MAPE) of the improved XGBoost–LSTM algorithm are decreased compared with those for the LSTM and XGBoost algorithms, among which the RMSE is reduced by 8.26% and 4.15% and the MAPE is reduced by 24.56% and 27.99%, respectively; its goodness-of-fit R2 value is closer to 1. This indicates that the algorithm proposed in this paper reduces the existing defects present in some current algorithms, and the prediction accuracy is effectively improved, which is of great value in improving the reliability of the system.

show abstract

Section: Previous Related Workmentioning

confidence: 99%

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

Gou,

Zhang,

Wen

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Doubly fed wind turbines are vulnerable to various types of generator losses; these failures lead to excess vibrations that might damage other components, such as bearing Hence, a solution for effective condition monitoring of generator bearings and early identification of failure symptoms is needed. From actual maintenance practices of wind turbine generators, it was found that there is a high nonlinear relationship between the turbine fault and relevant factors [11]. Therefore, without proper monitoring, replacement of the damaged bearing will not solve the problem and will cause damage again.…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique

et al. 2023

View full text Add to dashboard Cite

Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues.

show abstract

“…Wang et al [10] proposed a fault diagnosis method of wind turbine gearbox based on Riemannian manifold, which is fast in model training, but the accuracy of the model is not high. Zhang et al [11] proposed a wind turbine fault diagnosis method based on the Gaussian process metamodel, which has excellent performance. But the model has high dependence on dataset, which leads to poor generalization ability of the model.…”

Section: Introductionmentioning

confidence: 99%

Attention Mechanism‐Based CNN‐LSTM Model for Wind Turbine Fault Prediction Using SSN Ontology Annotation

Xie

Zhao²,

Qiang

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The traditional model for wind turbine fault prediction is not sensitive to the time sequence data and cannot mine the deep connection between the time series data, resulting in poor generalization ability of the model. To solve this problem, this paper proposes an attention mechanism-based CNN-LSTM model. The semantic sensor data annotated by SSN ontology is used as input data. Firstly, CNN extracts features to get high-level feature representation from input data. Then, the latent time sequence connection of features in different time periods is learned by LSTM. Finally, the output of LSTM is input into the attention mechanism module to obtain more fault-related target information, which improves the efficiency, accuracy, and generalization ability of the model. In addition, in the data preprocessing stage, the random forest algorithm analyzes the feature correlation degree of the data to get the features of high correlation degree with the wind turbine fault, which further improves the efficiency, accuracy, and generalization ability of the model. The model is validated on the icing fault dataset of No. 21 wind turbine and the yaw dataset of No. 4 wind turbine. The experimental results show that the proposed model has better efficiency, accuracy, and generalization ability than RNN, LSTM, and XGBoost.

show abstract

Fault Diagnosis Strategy for Wind Turbine Generator Based on the Gaussian Process Metamodel

Cited by 4 publications

References 28 publications

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique

Attention Mechanism‐Based CNN‐LSTM Model for Wind Turbine Fault Prediction Using SSN Ontology Annotation

Contact Info

Product

Resources

About