Abstract
Signal processing is of vital importance to the incipient fault diagnosis and the safety running of wind turbines. To adaptively eliminate noise and retain the underlying fault characteristic signal, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoid-based acceleration coefficients (SBAC-CDWPSO) is proposed in this paper. Firstly, a high-order continuous differentiable adaptive exponential threshold function (AETF) based on stein unbiased risk estimation is put forward to improve the defects of the traditional threshold functions. Secondly, the sine map and the sigmoid-based acceleration coefficients are applied for the velocity updating mechanism in particle swarm optimization (PSO). Meanwhile, the dynamic weight, the acceleration coefficient and the best-so-far position are introduced to update the new position with the previous position and the velocity in PSO. Moreover, the gaussian mutation strategy is added, which can effectively maintain the diversity of the swarm and get rid of local optimization. Thirdly, the SBAC-CDWPSO is used to optimize the threshold iteration process in AETF, which can greatly improve the iteration speed of the optimal threshold, and enhance the noise reduction effect. Experimental results showed that the signal-to-noise ratio of our proposed method was higher and the root mean square error was smaller comparing with the pre-existing algorithms. Moreover, wind turbine generator bearing fault diagnosis classification results illustrated that the fault diagnosis rate of the proposed denoising algorithm was up to 96.67%, indicating that the proposed method has great potential in the incipient fault diagnosis of wind turbine bearings.
Highlights
Generator is one of the most important parts in the wind turbine drive system, and it is prone to failure due to its high speed and large load fluctuation range
How to improve the noise reduction effect and fault diagnosis rate is the key to the incipient fault diagnosis, which is of great significance for the life prediction and operation reliability of wind turbines
The nonlinear soft-like thresholding function (NSTF) was applied to the test signal of electromechanical transmission system and the adaptive denoising of partial discharge signal respectively[18, 19], and the results showed that the NSTF could effectively remove the interference signal
Summary
Generator is one of the most important parts in the wind turbine drive system, and it is prone to failure due to its high speed and large load fluctuation range. Chen et al adopted wavelet spatial neighboring coefficient denoising with data-driven threshold to avoid the inaccurate identification the internal modes caused by the heavy noise, and the proposed method could identify fault feature of wind turbine generator bearing successfully[9]. The above research shows that wavelet analysis is a useful and effective time-frequency method for wind turbine incipient fault detection, and it is mainly applied to feature separation and noise elimination[11-13]. To solve the above problems, an adaptive exponential wavelet threshold denoising method based on chaotic dynamic weight particle swarm optimization with sigmoidbased acceleration coefficients called SBAC-CDWPSOAETF is proposed in this paper. (ii) To balance the global search ability in the early stage and the global convergence in the latter stage, a chaotic dynamic weight particle swarm optimization with sigmoidbased acceleration coefficients (SBAC-CDWPSO) is presented, and simulation experiments of four benchmark functions are conducted to validate its superiority. (iv)Wind turbine generator bearing fault diagnosis experiment illustrates that the fault diagnosis rate of the proposed algorithm is up to 96.67%, showing that the method has certain engineering application value for bearing early fault diagnosis of wind turbine
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