Adaptive Active Noise Cancellation Method of High Voltage Reactor Based on DnCNN Network

Current noise cancellation methods can barely deal with nonlinear signals in an effective way and the overall noise reduction performance is relatively poor. Aiming at these issues, we propose an adaptive active noise cancellation (ANC) method of high voltage reactors based on the denoising convolutional neural network (DnCNN). First, a feedforward adaptive ANC system was designed by considering the noise characteristics of high voltage reactors. Then, a noise control model was constructed and the weight coefficient of the filter was optimized by using the DnCNN network; meanwhile, the inverse waveform was reconstructed by adding spectrum analysis, thereby achieving effective control of noise signals. Additionally, the whale optimization algorithm was employed to solve the problem models of minimization of total radiation power of different sound sources, and the optimized location, amplitude, and phase angle of the secondary sound sources were determined. Based on LabVIEW, an online noise monitoring and ANC system was developed and used for experimental analysis of the proposed method. The results showed that the noise reduction performance was optimized (the overall noise reduction = 9 dB) when the number of secondary sound sources was 8 and the distance from the primary sound source was 1.0 m, suggesting that the proposed method can reliably reduce the noises of high voltage reactors.