Real Time Indoor Positioning System for Smart Grid based on UWB and Artificial Intelligence Techniques

被引:10
|
作者
Cheng, Long [1 ]
Chang, Hao [2 ]
Wang, Kexin [3 ]
Wu, Zhaoqi [4 ]
机构
[1] ABB Inc, Power Syst Consulting, Raleigh, NC 27606 USA
[2] Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, Troy, NY USA
[3] Univ Minnesota, Sch Math, Minneapolis, MN 55455 USA
[4] Univ Illinois, Dept Phys, Champaign, IL USA
关键词
indoor localization; ultra-wideband; artificial intelligence; RTLS; smart grid; LOCALIZATION; ENVIRONMENT; ALGORITHM; TOA;
D O I
10.1109/sustech47890.2020.9150486
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Indoor positioning system plays an important role in smart grid. Although GPS is the predominant outdoor positioning technology, it is unsuitable to be used in many fields of smart grid for three main reasons: first, signals sent from GPS could easily get blocked by solid materials such as metal or brick; second, the complex electromagnetic interference induced by electrical circuits greatly affects GPS signals; third, GPS can only achieve meter-level real time positioning accuracy, which is far from sufficient for many requirements of smart grid applications. Some other indoor positioning technologies, such as Bluetooth, Wi-Fi, ultrasound, infrared and RFID, fail in either the positioning accuracy, the positioning range, or the positioning speed required in many smart grid applications. Therefore, this paper proposes a real time indoor positioning system for smart gird based on a more promising technology, ultra-wideband (UWB). UWB is suitable for real-time localization in smart grid because UWB has short radio frequency pulse duration and wide bandwidth, which can minimize the effects of multipath interference and allow for high-resolution ranging and easier material penetration. In addition, since high-accuracy position information is required in many smart grid fields, a comprehensive framework integrating several artificial intelligence techniques, including outlier detection, line-of-sight/non-line-of-sight classification, filter design, range measurement correction and maximum likelihood localization estimation, is also proposed to further improve the positioning accuracy. At last, the performance of this system is verified through a series of experiments.
引用
收藏
页数:7
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