{"title":"LArcNet:用于实时串联交流电弧故障检测的轻量级神经网络","authors":"Kamal Chandra Paul;Chen Chen;Yao Wang;Tiefu Zhao","doi":"10.1109/OJIA.2024.3522364","DOIUrl":null,"url":null,"abstract":"Detecting series ac arc faults in diverse residential loads is challenging due to variations in load characteristics and noise. While traditional artificial intelligence-based algorithms can be effective, they often involve high computational complexity, limiting their real-time implementation on resource-constrained edge devices. This article introduces lightweight arc fault detection network (LArcNet), a novel, lightweight, and rapid-response algorithm for series ac arc fault detection. LArcNet combines a teacher–student knowledge distillation approach with an efficient convolutional neural network architecture to achieve high accuracy with minimal computational demand. This streamlined yet robust design makes LArcNet ideally suited for resource-constrained embedded systems, achieving an arc fault detection accuracy of 99.31%. The model is optimized and converted into TensorFlow Lite format to reduce size and latency, enabling deployment on low-power embedded devices such as the Raspberry Pi and the STM32 microcontrollers. Test results demonstrate LArcNet's inference times of just 0.20 ms on the Raspberry Pi 4B and 3 ms on the STM32H743ZI2, surpassing other leading models in operational speed while maintaining competitive accuracy in arc fault detection.","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"6 ","pages":"79-92"},"PeriodicalIF":7.9000,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816166","citationCount":"0","resultStr":"{\"title\":\"LArcNet: Lightweight Neural Network for Real-Time Series AC Arc Fault Detection\",\"authors\":\"Kamal Chandra Paul;Chen Chen;Yao Wang;Tiefu Zhao\",\"doi\":\"10.1109/OJIA.2024.3522364\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Detecting series ac arc faults in diverse residential loads is challenging due to variations in load characteristics and noise. While traditional artificial intelligence-based algorithms can be effective, they often involve high computational complexity, limiting their real-time implementation on resource-constrained edge devices. This article introduces lightweight arc fault detection network (LArcNet), a novel, lightweight, and rapid-response algorithm for series ac arc fault detection. LArcNet combines a teacher–student knowledge distillation approach with an efficient convolutional neural network architecture to achieve high accuracy with minimal computational demand. This streamlined yet robust design makes LArcNet ideally suited for resource-constrained embedded systems, achieving an arc fault detection accuracy of 99.31%. The model is optimized and converted into TensorFlow Lite format to reduce size and latency, enabling deployment on low-power embedded devices such as the Raspberry Pi and the STM32 microcontrollers. Test results demonstrate LArcNet's inference times of just 0.20 ms on the Raspberry Pi 4B and 3 ms on the STM32H743ZI2, surpassing other leading models in operational speed while maintaining competitive accuracy in arc fault detection.\",\"PeriodicalId\":100629,\"journal\":{\"name\":\"IEEE Open Journal of Industry Applications\",\"volume\":\"6 \",\"pages\":\"79-92\"},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2024-12-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816166\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of Industry Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10816166/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Industry Applications","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10816166/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
由于负载特性和噪声的变化,在各种住宅负载中检测串联交流电弧故障具有挑战性。虽然传统的基于人工智能的算法是有效的,但它们通常涉及高计算复杂性,限制了它们在资源受限的边缘设备上的实时实现。本文介绍了一种新颖、轻量、快速响应的串联交流电弧故障检测算法——轻型电弧故障检测网络(LArcNet)。LArcNet将师生知识蒸馏方法与高效的卷积神经网络架构相结合,以最小的计算需求实现高精度。这种精简而坚固的设计使LArcNet非常适合资源受限的嵌入式系统,实现了99.31%的电弧故障检测精度。该模型经过优化并转换为TensorFlow Lite格式,以减小尺寸和延迟,从而可以部署在低功耗嵌入式设备上,如树莓派和STM32微控制器。测试结果表明,LArcNet在Raspberry Pi 4B上的推断时间仅为0.20 ms,在STM32H743ZI2上的推断时间仅为3 ms,在运行速度上超过其他领先型号,同时在电弧故障检测方面保持具有竞争力的准确性。
LArcNet: Lightweight Neural Network for Real-Time Series AC Arc Fault Detection
Detecting series ac arc faults in diverse residential loads is challenging due to variations in load characteristics and noise. While traditional artificial intelligence-based algorithms can be effective, they often involve high computational complexity, limiting their real-time implementation on resource-constrained edge devices. This article introduces lightweight arc fault detection network (LArcNet), a novel, lightweight, and rapid-response algorithm for series ac arc fault detection. LArcNet combines a teacher–student knowledge distillation approach with an efficient convolutional neural network architecture to achieve high accuracy with minimal computational demand. This streamlined yet robust design makes LArcNet ideally suited for resource-constrained embedded systems, achieving an arc fault detection accuracy of 99.31%. The model is optimized and converted into TensorFlow Lite format to reduce size and latency, enabling deployment on low-power embedded devices such as the Raspberry Pi and the STM32 microcontrollers. Test results demonstrate LArcNet's inference times of just 0.20 ms on the Raspberry Pi 4B and 3 ms on the STM32H743ZI2, surpassing other leading models in operational speed while maintaining competitive accuracy in arc fault detection.