Noise Cancellation of a Train Electric Traction System Fan Based on a Fractional-Order Variable-Step-Size Active Noise Control Algorithm

Abstract

In this study, an active noise control (ANC) algorithm based on fractional order with a variable step size is proposed to improve the convergence performance of an integer-order ANC algorithm and achieve noise cancellation of a train electric traction system fan. The algorithm optimized the convergence parameters by constructing a time-varying function related to the variation of an error signal to improve the convergence speed of the algorithm, and the introduction of fractional calculus in the algorithm to control the update iteration of the filter weight coefficients, which could improve the control accuracy of the algorithm and reduce the steady-state error. And we give proof of the convergence of the algorithm and compared the noise reduction effect of the system under different orders to obtain the optimal order. The algorithm is compared with other traditional algorithms, and it is proved that it is superior to traditional algorithms in terms of convergence speed, steady-state error, and error attenuation. The experimental results demonstrate that the algorithm exhibited a good noise reduction effect on the fan noise of the train electric traction system of 0–500 Hz and the average noise reduction was about 1.9–4.1 dB.