scholarly journals Misalignment Fault Prediction of Wind Turbines Based on Improved Artificial Fish Swarm Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 692
Author(s):  
Zhe Hua ◽  
Yancai Xiao ◽  
Jiadong Cao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Frequency Domain ◽  
Wind Turbines ◽  
Development Trend ◽  
Fault Prediction ◽  
Support Vector ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Misalignment Fault

A misalignment fault is a kind of potential fault in double-fed wind turbines. The reasonable and effective fault prediction models are used to predict its development trend before serious faults occur, which can take measures to repair in advance and reduce human and material losses. In this paper, the Least Squares Support Vector Machine optimized by the Improved Artificial Fish Swarm Algorithm is used to predict the misalignment index of the experiment platform. The mixed features of time domain, frequency domain, and time-frequency domain indexes of vibration or stator current signals are the inputs of the Least Squares Support Vector Machine. The kurtosis of the same signals is the output of the model, and theprinciple of the normal distribution is adopted to set the warning line of misalignment fault. Compared with other optimization algorithms, the experimental results show that the proposed prediction model can predict the development trend of the misalignment index with the least prediction error.

scholarly journals Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 56 ◽  
Author(s):  
Yancai Xiao ◽  
Zhe Hua
Keyword(s):  
Frequency Domain ◽  
Wind Turbines ◽  
Working Environment ◽  
Fault Prediction ◽  
Support Vector ◽  
Forecasting Model ◽  
Term Operation ◽  
Time Frequency ◽  
Combined Forecasting ◽  
Misalignment Fault

Due to the harsh working environment of wind turbines, various types of faults are prone to occur during long-term operation. Misalignment faults between the gearbox and the generator are one of the latent common faults for doubly-fed wind turbines. Compared with other faults like gears and bearings, the prediction research of misalignment faults for wind turbines is relatively few. How to accurately predict its developing trend has always been a difficulty. In this paper, a combined forecasting model is proposed for misalignment fault prediction of wind turbines based on vibration and current signals. In the modelling, the improved Multivariate Grey Model (IMGM) is used to predict the deterministic trend and the Least Squares Support Vector Machine (LSSVM) optimized by quantum genetic algorithm (QGA) is adopted to predict the stochastic trend of the fault index separately, and another LSSVM optimized by QGA is used as a non-linear combiner. Multiple information of time-domain, frequency-domain and time-frequency domain of the wind turbine’s vibration or current signals are extracted as the input vectors of the combined forecasting model and the kurtosis index is regarded as the output. The simulation results show that the proposed combined model has higher prediction accuracy than the single forecasting models.

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