Abstract
In this study, the active noise control (ANC) method was used to suppress high-decibel and low-frequency power transformer noise. An appropriate ANC system was selected based on the transformer noise characteristics and experimental condition. A new filter-X least mean square (FXLMS) adaptive ANC algorithm based on offline and online secondary-path modeling was proposed to realize faster and more stable secondary-path online modeling than that of the random white-noise FXLMS algorithm and to ensure the convergence, stability, and reduction in transformer noise control. Moreover, the genetic algorithm is adopted to optimize the convergence coefficient, while the effect of the convergence coefficient on the algorithm was analyzed using simulation and theory. In addition, the transformer noise online monitoring and active control system was designed including software and hardware, and the hardware devices were selected based on the noise feature. In the 50 000 KVA transformer noise reduction experiment, the system achieved a noise reduction of 8–15 dB and an 84.10–96.86% decrease in average sound energy density in a certain area.
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