Lightweight Arc Fault Detection Method Based on Adam-Optimized Neural Network and Hardware Feature Algorithm

Wei Chen, Yi Han, Jie Zhao, Chong Chen (), Bin Zhang, Ziran Wu and Zhenquan Lin
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Wei Chen: Engineering Research Center of Low-Voltage Apparatus Technology of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
Yi Han: Engineering Research Center of Low-Voltage Apparatus Technology of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
Jie Zhao: Shanghai Hongtan Intelligent Technology Co., Ltd., Shanghai 201306, China
Chong Chen: Engineering Research Center of Low-Voltage Apparatus Technology of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
Bin Zhang: School of Mechanical Engineering, Zhejiang University, Hangzhou 310007, China
Ziran Wu: Engineering Research Center of Low-Voltage Apparatus Technology of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
Zhenquan Lin: Engineering Research Center of Low-Voltage Apparatus Technology of Zhejiang Province, Wenzhou University, Wenzhou 325035, China

Energies, 2024, vol. 17, issue 6, 1-14

Abstract: Arc faults are the main cause of electrical fires according to national fire data statistics. Intensive studies of artificial intelligence-based arc fault detection methods have been carried out and achieved a high detection accuracy. However, the computational complexity of the artificial intelligence-based methods hinders their application for arc fault detection devices. This paper proposes a lightweight arc fault detection method based on the discrimination of a novel feature for lower current distortion conditions and the Adam-optimized BP neural network for higher distortion conditions. The novel feature is the pulse signal number per unit cycle, reflecting the zero-off phenomena of the arc current. Six features, containing the novel feature, are chosen as the inputs of the neural network, reducing the computational complexity. The model achieves a high detection accuracy of 99.27% under various load types recommended by the IEC 62606 standard. Finally, the proposed lightweight method is implemented on hardware based on the STM32 series microcontroller unit. The experimental results show that the average detection accuracy is 98.33%, while the average detection time is 45 ms and the average tripping time is 72–201 ms under six types of loads, which can fulfill the requirements of real-time detection for commercial arc fault detection devices.

Keywords: arc fault detection; Adam-optimizer; neural networks; series arc fault (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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