RSSI-Power-Based Direction of Arrival Estimation of Partial Discharges in Substations

Fan Wu, Lingen Luo, Tingbo Jia, Anqing Sun, Gehao Sheng and Xiuchen Jiang
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Fan Wu: Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD., Minhang District, Shanghai 200240, China
Lingen Luo: Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD., Minhang District, Shanghai 200240, China
Tingbo Jia: Rizhao Power Supply Company, Shandong Electric Power Corporation, Rizhao 276800, China
Anqing Sun: Rizhao Power Supply Company, Shandong Electric Power Corporation, Rizhao 276800, China
Gehao Sheng: Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD., Minhang District, Shanghai 200240, China
Xiuchen Jiang: Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD., Minhang District, Shanghai 200240, China

Energies, 2019, vol. 12, issue 18, 1-14

Abstract: The localization of partial discharges in air-insulated substations using ultra-high frequency technology is widely studied for power equipment early warning purposes. Ultra-high frequency partial discharge localization systems are usually based on the time-difference of electromagnetic wave signals. However, the large size of test equipment and the need for a high sampling rate and time synchronization accuracy limit their practical application. To address this challenge, this paper proposes a power-based partial discharge direction of arrival method using a received signal strength indicator from an ultra-high frequency wireless sensor array. Furthermore, the Gaussian mixture model is used for noise suppression, and the Gaussian process classifier is used for line of sight received signal strength indicator data identification. Laboratory tests are performed and the results show the average error of direction of arrival is less than 5°. The results verify the effectiveness of the proposed partial discharge localization system.

Keywords: partial discharge; received signal strength indicator; direction of arrival; Gaussian mixture model; Gaussian process classifier (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: 2019
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