基于AI技术的电力现场作业安全预警系统关键技术研究  

Research on Key Technologies of Electric Power Field Operation Safety Early Warning System Based on AI Technology

作　　者：纪宏德 徐明 朱伟 JI Hongde;XU Ming;ZHU Wei(Jiaxing Power Supply Company of State Grid Zhejiang Electric Power Co.,Ltd.,Jiaxing 314000,China;Bochuang Materials Branch of Jiaxing Hengchuang Electric Power Group Co.,Ltd.,Jiaxing 314033,China)

机构地区：[1]国网浙江省电力有限公司嘉兴供电公司,浙江嘉兴314000 [2]嘉兴恒创电力集团有限公司博创物资分公司,浙江嘉兴314033

出　　处：《微型电脑应用》2025年第2期84-87,97,共5页Microcomputer Applications

基　　金：国网嘉兴供电公司集体企业科技项目(2019-LHKJ-015)。

摘　　要：针对电力现场作业面临状况复杂、安全预警能力差的问题,提出电力现场作业安全预警系统设计方案。安全预警系统采用Advanced RISC Machines(ARM)架构的64位和1.5 GHz的微处理器Cortex-A72构建多种硬件结构组建的安全预警系统,通过红外摄像头对电力现场作业进行数据采集和处理。通过改进遗传算法-粒子群优化(GA-PSO)算法提高故障诊断和报警的灵敏度,通过XGBoost融合模型提高电力作业数据信息抗干扰能力。试验结果表明,所提算法的数据采集能力强,数据计算精度高。Aimed at the problems of complex conditions and poor safety early-warning capabilities faced by power field operations,a safety early-warning system design scheme for power field operations is proposed.The safety early warning system is established by the hardware structure of Cortex-A72,a 64-bit and 1.5 GHz microprocessor with Advanced RISC Machines(ARM)architecture,and used infrared camera to collect and process the power field operation data.It improves the sensitivity of fault diagnosis and alarm by the improved genetic algorithm-particle swarm optimization(GA-PSO)particle swarm algorithm,and improves the anti-interference ability of the power operation data information through the XGBoost fusion model.The test results show that the proposed algorithem has strong data collection ability and high data calculation accuracy.

关 键 词：电力现场 安全预警 ARM架构 中央控制 故障诊断 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]