This paper presents a detailed investigation on an asymmetric magnetic-coupled bending-torsion piezoelectric energy harvester based on harmonic excitation. There is an eccentricity between the shape center of moving magnets and the axis of the piezoelectric beam, which results in the bending and torsion simultaneously in working condition. The distributed mathematical model is derived from the energy method to describe the dynamic characteristics of the harvester, and the correctness of the model is verified by experiments. To further demonstrate the improvement performance of the proposed energy harvester, the bending-torsion energy harvester (i.e. magnetic-coupled was not configured) is experimented and compared. The theoretical and experimental results indicate that the average power increases about 300% but the resonance frequency decreases approximately 2 Hz comparing to the harvester without magnetic-coupled. According to the characteristic of distributed parameter model, the magnetic force and the size of the piezoelectric beam are investigated respectively. And the lumped-parameter model is introduced to analyze the steady-state characteristic. Accordingly, this paper provides a feasible method to improve performance for piezoelectric energy harvester.
Vortex-induced vibration (VIV) is used by piezoelectric energy harvesters to generate electricity from wind and water flow. In this study, we introduce the nonlinear magnetic force into piezoelectric energy harvesters and develop a nonlinear monostable piezoelectric VIV transducer. We build a distributed-parameter model based on the Euler-Bernoulli beam theory and Kirchhof's law to analyze the dynamic responses of the magnetic-coupling piezoelectric energy harvester (MCPEH). Model results show that the performance of the MCPEH varies greatly with the increase of the load resistance and the length of the used PZT. There are two optimal resistance values for the MCPEH. When R<31.6 kΩ, both the external load resistance and the PZT length affect the maximum power output. The little optimum resistance value will dwindle with the increase of the PZT length, whereas the large optimum resistance value still fixes at 1.78 MΩ with the increase of the PZT length. Due to the resistive shunt damping effect and the kinetic energy of wind, the resonance domain becomes wider in these ranges of load resistance smaller than 31.6 kΩ and larger than 316 kΩ comparing that when the load resistance is larger than 31.6 kΩ and smaller than 316 kΩ. Besides, the performance is enhanced by the monostable nonlinear magnetic force, which can be improved by decreasing the value of the distance between moving magnets and fixed magnets. The energy harvester shows a maximum power output of 0.21 mW under excitation of wind velocity is 1.6 m/s when the cylindrical diameter is 20 mm, the PZT length is 30 mm and the load resistance is 0.5 MΩ.
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 to 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. And 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 preexisting 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.INDEX TERMS Adaptive wavelet threshold, dynamic weight particle swarm optimization, fault diagnosis, sigmoid-based acceleration coefficients, wind turbine bearing.
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