Optimization of noise barriers in a ±500 kV switchyard: Field testing and numerical modelling

Li  Li; Shenghui  Xu; Xing  Du; Cai  Zeng; Meng  Wei; Xinbiao  Xiao

doi:10.59400/sv2973

Optimization of noise barriers in a ±500 kV switchyard: Field testing and numerical modelling

Li Li Electric Power Research Institute, Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, 510000, China
Shenghui Xu State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
Xing Du School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
Cai Zeng School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China
Meng Wei Electric Power Research Institute, Guangdong Power Grid Co., Ltd., Guangzhou, Guangdong, 510000, China
Xinbiao Xiao State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China

Article ID: 2973

DOI: https://doi.org/10.59400/sv2973

Keywords: switchyard noise control; noise barrier; top structure; sound-absorbing materials; boundary element method

Abstract

This study investigates noise characteristics of a ±500 kV high-voltage (HV) switchyard through field testing. Noise levels and spectral properties near the reactor and fence were analyzed. An acoustic boundary element method (BEM) model was developed to predict the reactor-generated noise. Using this model, the effects of fence height, the height and placement of an additional noise barrier on the combined noise reduction effects (NRE) were systematically examined. To further enhance the NRE, various top structures and sound-absorbing materials were applied to noise barriers, and their additional NREs were compared. The results indicate that increasing fence height by 1–2 m reduces the sound pressure levels (SPL) outside the fence by 1–5.8 dB. Adding L-type or T-type top structures further reduces SPLs by 0.4–4.6 dB. Installing an additional erect noise barrier between the reactor and fence reduces SPLs by 1.4–8.8 dB, with the insertion loss (IL) increasing by 0.6–2.8 dB per 1-m barrier height increase. The T-type or Y-type top structures on the barriers also reduce SPLs by 0.4–4.6 dB, while traditional sound-absorbing materials have minimal impact on the NRE.

Published

2025-07-07

How to Cite

Li, L., Xu, S., Du, X., Zeng, C., Wei, M., & Xiao, X. (2025). Optimization of noise barriers in a ±500 kV switchyard: Field testing and numerical modelling. Sound & Vibration, 59(3), 2973. https://doi.org/10.59400/sv2973

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Issue

Vol. 59 No. 3 (2025)

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Li Z, Lang X, Yang B, et al. Vibration and noise mechanism of a 110 kV transformer under DC bias based on finite element method. Global Energy Interconnection. 2024; 7(4): 503–512. doi: 10.1016/j.gloei.2024.08.012

[2]Wang H, Zhang L, Sun Y, Zou L. Research on the influence mechanism of harmonic components on the noise distribution characteristics of converter transformers. International Journal of Electrical Power & Energy Systems. 2024; 160: 110095. doi: 10.1016/j.ijepes.2024.110095

[3]Borowik L, Włodarz R, Chwastek K. Eco-efficient control of the cooling systems for power transformers. Journal of Cleaner Production. 2016; 139: 1551–1562. doi: 10.1016/j.jclepro.2015.12.094

[4]Di GQ, Zhou XX, Chen XW. Annoyance response to low-frequency noise with tonal components: A case study on transformer noise. Applied Acoustics. 2015; 91: 40–46. doi: 10.1016/j.apacoust.2014.12.003

[5]Ying L, Wang D, Wang G, Wang W. Acoustic characteristic analysis of power transformers in urban communities based on a combined finite and boundary element method. Indoor and Built Environment. 2020; 29(2): 208–220.

[6]Liang P, Li J, Li Z, et al. Effect of low-frequency noise exposure on cognitive function: a systematic review and meta-analysis. BMC Public Health. 2024; 24(1): 125.

[7]Lu Y, Wu SF, Nie C, He W. Locating and reconstructing transformer low-frequency noises with a 3D, six-microphone array. Applied Acoustics. 2025; 228: 110351.

[8]Miao X, Jiang P, Pang F, et al. Numerical analysis and experimental research of vibration and noise characteristics of oil-immersed power transformers. Applied Acoustics. 2023; 203: 109189.

[9]Piana EA, Roozen NB. On the control of low-frequency audible noise from electrical substations: A case study. Applied Sciences. 2020; 10(2): 637–656.

[10]Banks JL, Hubal EAC. Noise: a public health problem. Journal of Exposure Science and Environmental Epidemiology. 2025; 35(1).

[11]Hahad O, Kuntic M, Al-Kindi S, et al. Noise and mental health: evidence, mechanisms, and consequences. Journal of Exposure Science & Environmental Epidemiology. 2024; 1–8.

[12]Bingham PM. Neurodevelopmental costs of noise pollution-is history rhyming again?. Journal of Exposure Science & Environmental Epidemiology. 2024; 1–3.

[13]Mehri A, Abbasi M. Noise exposure and job stress—A structural equation model in textile industries. Archives of Acoustics. 2020; 45(4): 601–611.

[14]Zhang J, Wang Y, Chen Y, et al. Estimation method of sound power of UHV transformer based on vibration and sound pressure measurements (Chinese). High Voltage Engineering. 2019; 45(6): 1843–1850. doi: 10.13336/j.1003-6520.hve.20190604021

[15]Wang D, Ying L, Jia Y, et al. Noise pollution mitigation method for substations in urban communities based on a smart silencing unit. Journal of Cleaner Production. 2020; 245: 118911. doi: 10.1016/j.jclepro.2019.118911

[16]Wang S, Tao J, Qiu X, Burnett IS. A natural ventilation window for transformer noise control based on coiled-up silencers consisting of coupled tubes. Applied Acoustics. 2022; 192: 108744. doi: 10.1016/j.apacoust.2022.108744

[17]Rausch M, Kaltenbacher M, Landes H, et al. Combination of finite and boundary element methods in investigation and prediction of load-controlled noise of power transformers. Journal of Sound and Vibration. 2002; 250(2): 323–338. doi: 10.1006/jsvi.2001.3934

[18]Wang L, Geng M, Bai X, et al. Urban 110 kV indoor substation noise analysis and control schemes: A real case study. Applied Acoustics. 2021; 183: 108290. doi: 10.1016/j.apacoust.2021.108290

[19]Fan X, Li L, Zhao L, et al. Environmental noise pollution control of substation by passive vibration and acoustic reduction strategies. Applied Acoustics. 2020; 165: 107305. doi: 10.1016/j.apacoust.2020.107305

[20]Di G, Jiang H, Chen C, et al. A study on the control indices and emission limits of substation noise. Applied Acoustics. 2023; 205: 109275. doi: 10.1016/j.apacoust.2023.109275

[21]Oliveira TDA, Acosta JJG, Cunha B, et al. Assessment of the acoustic field in the vicinity of urban high-voltage substations. Measurement: Sensors. 2021; 18: 100162.

[22]Sun T, Pei C, Hu J, et al. Noise source model and simulation analysis of UHV transformer. High Voltage Engineering. 2014; 40(9): 2750–2756.

[23]Ruan X, Wei H, Li Z. Model for predicting outdoor coherent noise and its engineering application. China Environmental Science. 2015; 35(6): 1877–1884.

[24]Gao Y. Research on the acoustic field characteristics and noise distribution prediction of substations [Master’s thesis] (Chinese). Beijing: North China Electric Power University; 2014.

[25]Wang X, Li W, Jin D. Research progress of noise control technology of substation. Electric Power Technology and Environmental Protection. 2017; 33(6): 34–37.

[26]Shi X, Lin Q, Kim J, et al. Optimization of noise control schemes based on improvement of annoyance (Chinese). China Environmental Science. 2018; 39(1): 397–401. doi: 10.19674/j.cnki.issn1000-6923.2019.0049

[27]Zhao X, Shuai Z, Zhang Y, Liu Z. Research on design method of metamaterial sound field control barrier based on transformer vibration and noise (Chinese). Energy Reports. 2022; 8: 1080–1089. doi: 10.19674/j.cnki.issn1000-6923.2019.0049

[28]Cao M, Mo J, Mo H, et al. Correlation analysis and prediction method on structure vibration and noise caused by transformer in underground substation. In: Proceedings of the CSEE; 2014.

[29]Yu S, Fu J, Cha Y. Noise analysis and low-noise design of power transformer (Chinese). Transformer. 2019; 56(9): 19–27. doi: 10.19487/j.cnki.1001-8425.2019.09.004

[30]Zhang L, Sun G, Shen J, et al. Design and experimental study of acoustical enclosure for reactor (Chinese). Journal of Harbin Engineering University. 2007; 28(12): 1352–1355+1381.

[31]Xu S. Design and analysis of interference noise reduction devices on low-height noise barriers near urban rail transit. Dissertation [Master’s thesis] (Chinese). Chengdu: Southwest Jiaotong University; 2019.

[32]Murata K, Nagakura K, Kitagawa T, et al. Noise reduction effect of noise barrier for Shinkansen based on Y-shaped structure. Quarterly Report of RTRI. 2006; 47(3): 162–168. doi: 10.2219/rtriqr.47.162

[33]May DN, Osman NM. The performance of sound absorptive, reflective, and T-profile noise barriers in Toronto. Journal of Sound & Vibration. 1980; 71(1): 73–101. doi: 10.1016/0022-460X(80)90409-5

[34]Yin H, Li Z, He C, et al. Interference noise reduction component for railway platforms. Patent CN105755973A, 28 April 2016.

[35]Veloso M, Pereira M, Godinho L, et al. Insertion loss prediction of sonic crystal noise barriers covered by porous concrete using the Method of Fundamental Solutions. Applied Acoustics. 2023; 211: 109543. doi: 10.1016/j.apacoust.2023.109543

[36]Qin M, Wang J, Deng Q, Cai J. Insert loss experiment and acoustic impedance theoretical study on the noise barrier with MPP-QRD top structure. Applied Acoustics. 2024; 221: 109996. doi: 10.1016/j.apacoust.2024.109996

[37]Tarrazó-Serrano D, Castiñeira-Ibáñez S, San Bautista A, et al. Optimization of acoustic subwavelength slit barrier to control low-frequency noise in industrial buildings. Journal of Building Engineering. 2025; 103: 111926. doi: 10.1016/j.jobe.2025.111926

[38]NSAI Standard. Electroacoustics - Sound level meters - Part 1: Specifications. NSAI Standard; 2013.

[39]He B, Xiao X, Zhou X, et al. Numerical simulation of acoustic performance of geometric shapes of high-speed railway noise barriers. Journal of Mechanical Engineering. 2016; 52(2): 99–107.

[40]Ge T, Gong Z, Wang ZZ. Derivation of the calculation formula for the sound pressure level radiated by a plane sound source of arbitrary shape (Chinese). Noise and Vibration Control. 1987; 02: 12–17.

[41]Attenborough K. Ground parameter information for propagation modeling. Acoustical Society of America Journal. 1992; 92(5): 3007.

[42]Du GH, Zhu ZM, Gong XF. Fundamentals of acoustics, 3rd edition (Chinese). Nanjing, China: Nanjing University press; 1981.

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