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Intelligent Substation Noise Monitoring System: Design, Implementation and Evaluation

Author

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  • Wenchen Chen

    (State Key Laboratory of Power Grid Environmental Protection, Wuhan 430074, China
    School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yingdong Liu

    (State Key Laboratory of Power Grid Environmental Protection, Wuhan 430074, China
    School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yayu Gao

    (State Key Laboratory of Power Grid Environmental Protection, Wuhan 430074, China
    School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Jingzhu Hu

    (State Key Laboratory of Power Grid Environmental Protection, Wuhan 430074, China)

  • Zhenghai Liao

    (State Key Laboratory of Power Grid Environmental Protection, Wuhan 430074, China)

  • Jun Zhao

    (State Key Laboratory of Power Grid Environmental Protection, Wuhan 430074, China)

Abstract

In recent years, the State Grid of China has placed significant emphasis on the monitoring of noise in substations, driven by growing environmental concerns. This paper presents a substation noise monitoring system designed based on an end-network-cloud architecture, aiming to acquire and analyze substation noise, and report anomalous noise levels that exceed national standards for substation operation and maintenance. To collect real-time noise data at substations, a self-developed noise acquisition device is developed, enabling precise analysis of acoustic characteristics. Moreover, to subtract the interfering environmental background noise (bird/insect chirping, human voice, etc.) and determine if noise exceedances are originating from substation equipment, an intelligent noise separation algorithm is proposed by leveraging the convolutional time-domain audio separation network (Conv-TasNet), dual-path recurrent neural network (DPRNN), and dual-path transformer network (DPTNet), respectively, and evaluated under various scenarios. Experimental results show that (1) deep-learning-based separation algorithms outperform the traditional spectral subtraction method, where the signal-to-distortion ratio improvement (SDRi) and the scale-invariant signal-to-noise ratio improvement (SI-SNRi) of Conv-TasNet, DPRNN, DPTNet and the traditional spectral subtraction are 12.6 and 11.8, 13.6 and 12.4, 14.2 and 12.9, and 4.6 and 4.1, respectively; (2) DPTNet and DPRNN exhibit superior performance in environment noise separation and substation equipment noise separation, respectively; and (3) 91% of post-separation data maintains sound pressure level deviations within 1 dB, showcasing the effectiveness of the proposed algorithm in separating interfering noises while preserving the accuracy of substation noise sound pressure levels.

Suggested Citation

  • Wenchen Chen & Yingdong Liu & Yayu Gao & Jingzhu Hu & Zhenghai Liao & Jun Zhao, 2024. "Intelligent Substation Noise Monitoring System: Design, Implementation and Evaluation," Energies, MDPI, vol. 17(13), pages 1-24, June.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3083-:d:1420167
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    References listed on IDEAS

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    1. Mohammed A. Shams & Hussein I. Anis & Mohammed El-Shahat, 2021. "Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform," Energies, MDPI, vol. 14(20), pages 1-22, October.
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