# Application Study of Adaptive Tracking Algorithm in Active Noise Control System of Transformer
## 1 Introduction  
Transformer is one of the most important equipment in power system [1]. The noises, especially the low frequency noises emitted by large power transformers and distribution transformers cause a serious disturbance to the surrounding community. For the traditional noise reduction methods, it is difficult to reduce the noise effectively due to the restrictions of economy, security, and other factors. It is believed that the active noise control (ANC) technology can obtain the considerable progress and attention of noise reduction applications in substations. ANC technology has been successfully applied in acoustic noise reduction, such as cars, ships, and other limited enclosed space [2]. In recent years, substation noise pollution has gained more social attention, and the application of ANC for this kind of free sound field has been gradually increased. It should be noted that ANC system control algorithms and secondary sound source placement are the two key points, which can help to improve the performance of noise reduction effectively. It is necessary to consider the noise characteristics while choosing the control algorithm. The steady-state error, convergence rate, and calculation delay of the algorithm are taken as the evaluation criteria, and the optimal setup is obtained in theory [3]. However, the acoustic reflection, diffraction, and acoustic waves coupling phenomenon in the free sound field make it difficult to achieve effective noise reduction in global space [2]. Therefore, in order to achieve an optimal local optimum, it is necessary to combine the acoustic radiation characteristics of the application environment and the specific noise reduction requirements. At present, ANC technology research on transformer focus on the least mean square (LMS) and its derivative algorithms. Such algorithms are sensitive to interference sources and acoustic feedback phenomena. So, there are many technical problems in the application of such algorithms in transformer noise reduction. In addition, existing research is carried out only in simulation or inanechoic chamber. So the effectiveness of noise reduction actual application in a substation cannot be proved. At present, many algorithms have been proposed for ANC systems, mainly the LMS algorithm and its improved algorithm. The effectiveness of this type algorithm will be adversely affected when there are other interference noise sources. The feedback branch in the control algorithm and the phenomenon of acoustic feedback in the actual system can also cause the system to be unstable. [PERSON] pointed out that the LMS algorithm is simple to calculate and easy to implement, but the disadvantage is the slow convergence [3]. The step size parameter should be chosen in two performance indexes: steady-state error, and convergence speed. [PERSON] applied the Filter-x least mean square (FxLMS) algorithm to transformer noise reduction for the first time, and discussed the distributions of noise and secondary sound sources [4]. However, it only used a 100 Hz sinusoidal signal as the analog noise source, ignoring the interference problem from picking up the noise source signal in practical applications. [PERSON] proposed an FxLMS control algorithm for the interference of the fundamental and 2 nd harmonic noise, and performed noise reduction experiments in a laboratory environment [5]. [PERSON] and [PERSON] proposed a waveform synthesis algorithm is proposed by using a notch filter to synthesize the secondary path transfer function based on the FxLMS algorithm, which improves the dependence of the FxLMS algorithm performance on secondary channel parameters [6]. [PERSON] used a small transformer as the noise source, the 100, 200, and 300 Hz noise components are taken as the targets to be eliminated, and the obvious noise reduction effect is achieved on the 100 Hz component [7]. The experiment is carried out in an anechoic chamber.