Abstract
By collecting the ripple current in the control coil, the self-sensing magnetic bearings can obtain rotor displacement information without a displacement sensor, which has the advantages of low cost and high integration. According to the problems of incomplete and inaccurate displacement information extraction caused by the traditional displacement estimation method using Fourier method to analyze the ripple current with non-stationary characteristics to estimate the rotor displacement with the Gibbs effect. Therefore, this paper proposes a rotor displacement estimation algorithm based on multiresolution filter bank biorthogonal spline wavelet for a magnetic bearing motor based on two-level switching power amplifier. This algorithm utilizes the biorthogonal spline wavelet with the characteristics of generalized linear phase and tight support, which can accurately demodulate the ripple current in the coil and extract the displacement information in the ripple current. Therefore, it is able to overcome the Gibbs effect in the traditional Fourier analysis estimation algorithm, to reduce the influence of ripple on rotor displacement estimation, and to improve the accuracy and stability of displacement estimation. In order to verify the correctness and effectiveness of the proposed algorithm, this paper establishes a simulation model of magnetic bearing in MATLAB Simulink and designs the proposed displacement estimator. Simulation results demonstrate that the proposed algorithm has higher accuracy and better stability than traditional displacement estimation algorithms. Experimental results show that the maximum deviation rate of the displacement estimation method proposed in this paper does not exceed 1% when the radial magnetic bearing gap is 0.5 mm, which can effectively estimate the rotor displacement.
Highlights
Active magnetic bearings have a wide range of application prospects in high-speed rotating machinery due to their excellent characteristics such as no friction, no lubrication, low loss, and high speed [1], [2]
Literature [8], [9] first proposed to establish a state observer with coil current as the input and rotor displacement as the output based on the state space model of the magnetic bearing system to realize the self-sensing operation of the voltage-controlled magnetic bearing system
To solve the above-mentioned problems, this paper proposes a rotor displacement estimation algorithm based on the biorthogonal spline wavelet (BSW) of the multiresolution filter bank
Summary
Active magnetic bearings have a wide range of application prospects in high-speed rotating machinery due to their excellent characteristics such as no friction, no lubrication, low loss, and high speed [1], [2]. Literature [8], [9] first proposed to establish a state observer with coil current as the input and rotor displacement as the output based on the state space model of the magnetic bearing system to realize the self-sensing operation of the voltage-controlled magnetic bearing system. The nonlinear parameter estimation method uses the function relationship between the amplitude of the ripple current generated by the high-frequency switching of the switching power amplifier and the equivalent inductance of the coil under the condition of a constant duty cycle to estimate the real-time displacement of the rotor. By selecting different finite length attenuation wavelet base expansion and translation, the multiscale refined analysis of the signal is carried out, and the information extraction of nonstationary signal ripple current and displacement estimation of self-sensing magnetic bearing are realized. It is suitable for the analysis of instantaneous nonstationary signals
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