Intelligent Substation Noise Monitoring System: Design, Implementation and Evaluation

Wenchen Chen, Yingdong Liu, Yayu Gao (), Jingzhu Hu, Zhenghai Liao and Jun Zhao
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Wenchen Chen: State Key Laboratory of Power Grid Environmental Protection, Wuhan 430074, China
Yingdong Liu: State Key Laboratory of Power Grid Environmental Protection, Wuhan 430074, China
Yayu Gao: State Key Laboratory of Power Grid Environmental Protection, 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

Energies, 2024, vol. 17, issue 13, 1-24

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.

Keywords: noise monitoring; substation noise separation; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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