Abstract
This paper studies a fault prediction method for wind turbine gearbox. It uses grey relation analysis to get modeling variables, and makes sample data getting good integrity and redundancy by similarity analysis. Thus it gets the reduced process memory matrix, and trains the improved nonlinear state estimation (NEST) model. When the gearbox fails, the model residual will exceed the threshold value, and the model will give an early warning. Combined with the actual operation data of a wind turbine, the effectiveness and accuracy of the improved model are verified.
Highlights
The gearbox as the key component of wind turbines can transfer the moment generated by wind energy to the generator and convert into electrical energy
The process memory matrix is not optimized for sample, and which affects the timeliness of model
This paper uses nonlinear state estimation (NEST) model to monitor the gearbox bearing temperature. It is aimed at the deficiency of selecting observation vector based on experience, and it uses the grey correlation analysis method to provide theoretical basis
Summary
The gearbox as the key component of wind turbines can transfer the moment generated by wind energy to the generator and convert into electrical energy. Literature [5] uses the nonlinear state estimation model to predict gearbox temperature. It selects observation variables relying on experience, and it lacks theoretical basis. This paper uses NEST model to monitor the gearbox bearing temperature. It is aimed at the deficiency of selecting observation vector based on experience, and it uses the grey correlation analysis method to provide theoretical basis. In order to ensure the integrity and small redundancy of data, this paper uses the similarity analysis method to construct the process memory matrix which can realize sample optimization. This paper uses SCADA data of a wind turbine to simulate and analysis, the results show that the improved NEST model can effectively predict the bearing temperature and has good timeliness
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