On the Performance of Active Noise Control FX-LMS and FBFX-LMS Algorithms for Duct Network Noise Attenuation

The problem of noise in power transformer was pay attention to by this paper. The paper presents the design methodology for the active noise control (ANC) of sound disturbances in power transformer. The active noise attenuation algorithm uses the framework of output-error based optimization of a linearly parameterized filter for feedforward sound compensation to select optimal location of sensor and demonstrate the effectiveness of active noise attention in a large power transformer. The ANC controller can automatically measure the sound disturbances and select the compensate parameters to realize the noise cancellation. With 220kV power transformer noise cancellation, for example, the simulating results prove that the ANC technology to cancel the noise in power transformer is an effective way.

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103 Low Frequency Noise Attenuation from a Soundproof House by Active Noise Control

The Proceedings of the Symposium on Environmental Engineering ◽

10.1299/jsmeenv.2012.22.18 ◽

2012 ◽

Vol 2012.22 (0) ◽

pp. 18-19

Author(s):

Toshiyuki AOKI ◽

Ikutarou SUGIURA ◽

Masanori TANAKA

Keyword(s):

Noise Control ◽

Active Noise Control ◽

Low Frequency ◽

Frequency Noise ◽

Noise Attenuation ◽

Low Frequency Noise ◽

Active Noise

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Improving Adaptive Filters for Active Noise Control Using Particle Swarm Optimization

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2018100103 ◽

2018 ◽

Vol 9 (4) ◽

pp. 47-64

Author(s):

Rodrigo P. Monteiro ◽

Gabriel A. Lima ◽

José P. G. Oliveira ◽

Daniel S. C. Cunha ◽

Carmelo J. A. Bastos-Filho

Keyword(s):

Particle Swarm Optimization ◽

Response Time ◽

Adaptive Filters ◽

Noise Control ◽

Active Noise Control ◽

Particle Swarm ◽

Frequency Noise ◽

Noise Attenuation ◽

Swarm Optimization ◽

Active Noise

The excessive exposure to certain kinds of acoustic noise can lead to health problems. To avoid this situation, the use of noise attenuation devices is a standard solution. Among those devices, the active noise control (ANC) systems have gained prominence over the years, mainly due to the technological development and costs reduction of electronic components. Despite good performance of ANC concerning low-frequency noise attenuation, the convergence speed for this kind of system is still an important issue when it deals with real-time applications in dynamic environments. This article presents an alternative solution to accelerate the active attenuation system response. This solution is based on the use of sets of coefficients, which are employed during the adaptive filter initialization and are obtained via a training process with particle swarm optimization (PSO). Two objective functions were tested: one based on the response time itself and the other one based on the magnitude reduction of the residual noise. The coefficients obtained through this process provided response time reductions up to 98.3% concerning adaptive filters initialized with null coefficients. The article is an extended version of the conference paper Accelerating the Convergence of Adaptive Filters for Active Noise Control Using Particle Swarm Optimization, published in LA-CCI 2017.

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Active Noise Control System Based on the Improved Equation Error Model

Acoustics ◽

10.3390/acoustics3020024 ◽

2021 ◽

Vol 3 (2) ◽

pp. 354-363

Author(s):

Jun Yuan ◽

Jun Li ◽

Anfu Zhang ◽

Xiangdong Zhang ◽

Jia Ran

Keyword(s):

Control System ◽

Noise Reduction ◽

Mean Square Error ◽

Noise Control ◽

Active Noise Control ◽

Noise Attenuation ◽

Mean Square ◽

Path Modeling ◽

Active Noise ◽

Equation Error

This paper presents an algorithm structure for an active noise control (ANC) system based on an improved equation error (EE) model that employs the offline secondary path modeling method. The noise of a compressor in a gas station is taken as an example to verify the performance of the proposed ANC system. The results show that the proposed ANC system improves the noise reduction performance and convergence speed compared with other typical ANC systems. In particular, it achieves 28 dBA noise attenuation at a frequency of about 250 Hz and a mean square error (MSE) of about −20 dB.

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