Abstract
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 the principle 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.
Highlights
Entropy 2021, 23, 692. https://Energy shortage and environmental pollution have become two great challenges for human beings
The Least Squares Support Vector Machine (LSSVM) optimized by improved artificial fish swarm algorithm (IAFSA) is used as the prediction model of misalignment fault for wind turbines in this paper
The Artificial Fish Swarm Algorithm (AFSA) is improved and it is used to optimize the parameters in LSSVM to predict the kurtosis index of vibration signals and stator current signals for wind turbines misalignment fault
Summary
Energy shortage and environmental pollution have become two great challenges for human beings. Reference [4] collected 50 real vibration data sets for analysis on a 2 MW wind turbine operating at the degradation of bearing performance, and the results showed that the regression model effectively improved the prediction performance of the artificial neural network. Reference [5] used the gearbox fault data obtained by the wind turbine monitoring and data acquisition system, using the confidence interval as the performance index, and proposed a new fault diagnosis and prediction method based on the support vector regression model. Reference [7] was based on the vibration signal collected on a test bench simulating a wind turbine, and adopted support vector machine to effectively identify misalignment and imbalance faults.
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