Abstract：RFID tags of traditional power equipment was used only for identification, lacking the ability about sense and interaction of multidimensional data. Based on the IoT technology, an eRFID tag with sense and data interaction capabilities was designed for the wide distribution and various types of power assets which need online awareness ability. The eRFID tag can not only used to the intelligent management about the life cycle of power assets, but also can effectively monitor parameters such as temperature, humidity and vibration to bring about integration of identification and perception. The eRFID online sense tag connect the control part with the RF part through the 915MHz UHF dual-interface RFID chip, realizing the combination of the device ID and its operate parameters, and can actively control the modules of eRFID online sense tag. The eRFID sense tag can monitor the vibration signal. When the vibration signal is abnormal, the eRFID tag waked up, and then it calculates the angular posture of the device. Then it complete alarms threshold, RTLS, information pushes to prevent device damage and theft. It uses Beidou positioning technology and a low-power micro controller unit (MCU) with Bluetooth to achieve indoor and outdoor omnidirectional position of power equipment. The tag also has built-in large-capacity storage to store complete information such as assets, drawings, etc., so that it establishes a micro-database to realize network topology recognition based on information definition.
张鋆, 张明皓, 仝杰, 李荡, 雷煜卿, 张树华. 用于电力资产在线感知的eRFID标签设计[J]. 电工技术学报, 2020, 35(11): 2296-2305.
Zhang Jun, Zhang Minghao, Tong Jie, Li Dang, Lei Yuqing, Zhang Shuhua. A eRFID Tag Design for Online Perception of Power Assets. Transactions of China Electrotechnical Society, 2020, 35(11): 2296-2305.
[1] 刘晟源, 林振智, 李金城, 等. 电力系统态势感知技术研究综述与展望[J]. 电力系统自动化, 2020, 44(3): 229-239. Liu Shengyuan, Lin Zhenzhi, Li Jincheng, et al.Review and prospect of situation awareness technologies of power system[J]. Automation of Electric Power Systems, 2020, 44(3): 229-239. [2] 张涛, 赵东艳, 薛峰, 等. 电力系统智能终端信息安全防护技术研究框架[J]. 电力系统自动化, 2019, 43(19): 1-8, 67. Zhang Tao, Zhao Dongyan, Xue Feng, et al.Research framework of cyber-security protection technologies for smart terminals in power system[J]. Automation of Electric Power Systems, 2019, 43(19): 1-8, 67. [3] 宋欢. 电力调度自动化系统中RFID接入技术及应用[D]. 北京: 北京邮电大学, 2018. [4] 吴欣, 高正浩, 陈俣霏, 等. 基于RFID与三维可视化技术的固定资产管理系统设计[J]. 电子设计工程, 2018, 26(18): 107-112. Wu Xin, Gao Zhenghao, Chen Yufei, et al.Design of asset management system based on RFID and 3D visualization technology[J]. Electronic Design Engineering, 2018, 26(18): 107-112. [5] 赵永柱, 任晓龙, 房涛. 基于物联网RFID技术的电网资产精益化管理研究[J]. 自动化与仪器仪表, 2018(3): 7-9. Zhao Yongzhu, Ren Xiaolong, Fang Tao.Research on lean management of grid assets based on internet of things RFID technology[J]. Automation & Instrumentation, 2018(3): 7-9. [6] 邓伟, 杜国宏, 周芹, 等. 基于改进型共面波导UHF RFID小型化抗金属标签设计[J]. 成都信息工程大学学报, 2019, 34(3): 219-222. Deng Wei, Du Guohong, Zhou Qin, et al.Design of improved coplanar waveguide for UHF RFID miniaturized maaetal skin tag[J]. Journal of Chengdu University of Information Technology, 2019, 34(3): 219-222. [7] 李勇, 黄庆林, 谷璐璐, 等. 一种双电压合成信号脉宽调制的低功耗高速电磁铁驱动电路[J]. 电工技术学报, 2019, 34(2): 255-263. Li Yong, Huang Qinglin, Gu Lulu, et al.A high-speed solenoid drive circuit with low power consumption based on pulse width modulation by dual voltage synthesized[J]. Transactions of China Electrotechnical Society, 2019, 34(2): 255-263. [8] 姚引娣, 王磊, 海小娟, 等. 基于农业物联网的低功耗智能温室监控系统[J]. 西安邮电大学学报, 2019, 24(2): 78-83. Yao Yidi, Wang Lei, Hai Xiaojuan, et al.Low power intelligent greenhouse monitoring system based on agricultural internet of things[J]. Journal of Xi’an University of Posts and Telecommunications, 2019, 24(2): 78-83. [9] 何号. 连接式低功耗BLE Mesh组网研究与设计[D]. 武汉: 华中科技大学, 2019. [10] 谭晖. 低功耗蓝牙开发与实战[M]. 北京: 北京航空航天大学出版社, 2015. [11] Huang Baichuan, Liu Jingbin, Sun Wei, et al.A robust indoor positioning method based on Bluetooth low energy with separate channel information[J]. Sensors, 2019, 19(16):83-86. [12] 康明涛, 张峰, 梁源, 等. 基于数据挖掘BLE指纹室内定位设计与实现[J]. 自动化与仪表, 2019, 34(4): 95-99. Kang Mingtao, Zhang Feng, Liang Yuan, et al.Design and implementation of BLE fringerprint indoor position based on data mining[J]. Automation & Instrumentation, 2019, 34(4): 95-99. [13] 黄正宇, 蒋鑫龙, 刘军发, 等. 基于融合特征的半监督流形约束定位方法[J]. 浙江大学学报: 工学版, 2017, 51(4): 655-662. Huang Zhengyu, Jiang Xinlong, Liu Junfa, et al.Fusion feature based semi-supervised manifold localization method[J]. Journal of Zhejiang University: Engineering Science, 2017, 51(4): 655-662. [14] 侯欣宾, 王立, 李庆民, 等. 空间太阳能电站高压大功率电力传输关键技术综述[J]. 电工技术学报, 2018, 33(14): 3385-3395. Hou Xinbin, Wang Li, Li Qingmin, et al.Review of key technologies for high-voltage and high-power transmission in space solar power station[J]. Transactions of China Electrotechnical Society, 2018, 33(14): 3385-3395. [15] Hwa-Young Yang, Sang Hun Lee, Hyung-Mo Kim, et al.Plasmonic and charging effects in dye-sensitized solar cells with Au nanoparticles incorporated into the channels of freestanding TiO2 nanotube arrays by an electrode position method[J]. Journal of Industrial and Engineering Chemistry, 2019, 31(11): 215-218. [16] 商立群, 朱伟伟. 基于全局学习自适应细菌觅食算法的光伏系统全局最大功率点跟踪方法[J]. 电工技术学报, 2019, 34(12): 2606-2614. Shang Liqun, Zhu Weiwei.Photovoltaic system global maximum power point tracking method based on the global learning adaptive bacteria foraging algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(12): 2606-2614. [17] 倪雨, 郝帅翔. 阻增量光伏动态模型研究[J]. 电机与控制学报, 2014, 18(6): 96-101. Ni Yu, Hao Shuaixiang.Study of solar dynamic model based on resistance increment[J]. Electric Machines and Control, 2014, 18(6): 96-101. [18] 颜湘武, 邓浩然, 郭琪, 等. 基于自适应无迹卡尔曼滤波的动力电池健康状态检测及梯次利用研究[J]. 电工技术学报, 2019, 34(18): 3937-3948. Yan Xiangwu, Deng Haoran, Guo Qi, et al.Study on the state of health detection of power batteries based on adaptive unscented Kalman filters and the battery echelon utilization[J]. Transactions of China Electrotechnical Society, 2019, 34(18): 3937-3948. [19] 范兴明, 王超, 张鑫, 等. 基于增量学习相关向量机的锂离子电池SOC预测方法[J]. 电工技术学报, 2019, 34(13): 2700-2708. Fan Xingming, Wang Chao, Zhang Xin, et al.A prediction method of Li-ion batteries SOC based on incremental learning relevance vector machine[J]. Transactions of China Electrotechnical Society, 2019, 34(13): 2700-2708. [20] 刘艳霞, 方建军, 石岗. 基于改进极限学习机的三轴加速度计误差补偿算法[J]. 传感器与微系统, 2019, 38(7): 138-141. Liu Yanxia, Fang Jianjun, Shi Gang.Three-axis accelerometer error compensation algorithm based on improved extreme learning machine[J]. Transducer and Microsystem Technologies, 2019, 38(7): 138-141. [21] Johnny Rodriguez-Maldonado.Estimation of angular velocity and acceleration with Kalman filter, based on position measurement only[J]. Measurement, 2019, 145: 130-136. [22] 胡思健. 基于蓝牙低功耗的室内定位算法改进与系统实现研究[D]. 广州: 华南理工大学, 2017. [23] 刘振远. 基于蓝牙信标和智能手机的室内位置服务关键技术研究[D]. 北京: 北京交通大学, 2018. [24] 代孝俊, 杜国宏, 邓伟, 等. 用于手术刀的抗金属无源RFID标签天线设计[J]. 微波学报, 2019, 35(2): 60-63. Dai Xiaojun, Du Guohong, Deng Wei, et al.Design of a metal skin passive RFID tag antenna for scalpel[J]. Journal of Microwaves, 2019, 35(2): 60-63. [25] Aslam B, Kashif M, Awais Azam M, et al.A low profile miniature RFID tag antenna dedicated to IoT applications[J]. Electromagnetics, 2019, 39(6): 393-406. [26] 孔繁荣, 李博文, 聂秋月, 等. 薄层等离子体增强微波接收特性实验及数值仿真[J]. 电机与控制学报, 2019, 23(4): 20-27. Kong Fanrong, Li Bowen, Nie Qiuyue, et al.Experimental and numerical studies on enhancement of microwave receiver characteristics by a thin plasma layer[J]. Electric Machines and Control, 2019, 23(4): 20-27.
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