Article: Deep learning-based comprehensive monitor for smart power station Journal: International Journal of Grid and Utility Computing (IJGUC) 2021 Vol.12 No.4 pp.380 - 387 Abstract: With the wider distribution of power substations, monitoring and control of substations at large scale has become more difficult by solely relying on manpower inspection. Smart monitoring systems are increasingly important to realise fast response, low-cost maintenance and autonomous control. In this paper, we develop a novel inspection system based on deep learning and edge computing techniques. Firstly, the on-site video acquisition is completed by drones only when abnormal situations are detected, realising flexible and low-cost inspection. Using deep Q-learning, we design an efficient and reliable navigation algorithm that guides drones to the target location with minimum human intervention. To reduce the response latency and support large-scale data processing, we take the advantages of edge computing and build a high-performance edge system. Moreover, several strategies from algorithm to hardware are proposed to optimise the processing pipeline of constructed edge computing system. The experiment and simulation results demonstrate the reliability and efficiency of our proposed system in the case of autonomous substation monitoring. Inderscience Publishers - linking academia, business and industry through research

Title: Deep learning-based comprehensive monitor for smart power station

Authors: Yerong Zhong; Guoheng Ruan; Jiaming Jiang

Addresses: Department of Information Technology, Qingyuan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Qingyuan, Guangdong, China ' Department of Information Technology, Qingyuan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Qingyuan, Guangdong, China ' Department of Information Technology, Qingyuan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Qingyuan, Guangdong, China

Abstract: With the wider distribution of power substations, monitoring and control of substations at large scale has become more difficult by solely relying on manpower inspection. Smart monitoring systems are increasingly important to realise fast response, low-cost maintenance and autonomous control. In this paper, we develop a novel inspection system based on deep learning and edge computing techniques. Firstly, the on-site video acquisition is completed by drones only when abnormal situations are detected, realising flexible and low-cost inspection. Using deep Q-learning, we design an efficient and reliable navigation algorithm that guides drones to the target location with minimum human intervention. To reduce the response latency and support large-scale data processing, we take the advantages of edge computing and build a high-performance edge system. Moreover, several strategies from algorithm to hardware are proposed to optimise the processing pipeline of constructed edge computing system. The experiment and simulation results demonstrate the reliability and efficiency of our proposed system in the case of autonomous substation monitoring.

Keywords: UAV; deep reinforcement learning; power substation control; edge computing.

DOI: 10.1504/IJGUC.2021.119564

International Journal of Grid and Utility Computing, 2021 Vol.12 No.4, pp.380 - 387

Received: 25 Jul 2020
Accepted: 25 Aug 2020
Published online: 09 Dec 2021 *

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Title: Deep learning-based comprehensive monitor for smart power station

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