New Inspection Method and Experience by Introducing HoloLens into Information Fusion and Data Mining

doi:10.11930/j.issn.1004-9649.201909115

Abstract

Abstract: At present, most inspection methods for substation have the problem of incomplete information acquisition, which is mainly reflected in two aspects: the loss of information caused by improper preservation of inspection information and the waste of information caused by the inability of the inspection system to obtain SCADA data in time. With the development of smart grid, power big data has more and more applications in equipment state estimation and so on. As a result, the contradiction between the data demand of the intelligent inspection system and the incomplete data acquisition occurs, and information fusion and data mining become much more important. The concept of Ubiquitous Internet of Things in Energy can be applied to the field of inspection. In this paper, the MR equipment HoloLens is used for substation inspection, and is used to connect people and objects. As a mobile human-computer interaction interface, the HoloLens seamlessly connects data and reality. The intelligent technology is used to acquire and store inspection data and call SCADA data to realize data flow interconnection. Based on the comprehensive information, the equipment condition assessment and failure rate calculation are carried out to provide auxiliary information for inspectors, consequently improving the quality and efficiency of inspection, and the new inspection contents and inspection experience are thus obtained. Finally, the results of user experience feedback from patrol inspectors are evaluated by fuzzy comprehensive evaluation, and the ideal results are achieved.

Key words: assistant inspection, MR, HoloLens, information fusion, condition assessment

GUO Guodong, GONG Yanfeng, WU Di, PAN Jianqiao, YU Fangzhao, PAN Bailang, LI Huan. New Inspection Method and Experience by Introducing HoloLens into Information Fusion and Data Mining[J]. Electric Power, 2021, 54(7): 208-216.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201909115

https://www.electricpower.com.cn/EN/Y2021/V54/I7/208

References

[1] 钟连宏, 梁贵书, 陈洪海, 等. 无人值班变电站巡视周期的仿真计算[J]. 高电压技术, 2007, 33(7):163-165, 205
ZHONG Lianhong, LIANG Guishu, CHEN Honghai, et al. Simulation of inspection period for non-attended power substation[J]. High Voltage Engineering, 2007, 33(7):163-165, 205
[2] 陶莉, 朱小光, 王善红. 变电站手持终端巡检方案界面设计[J]. 华电技术, 2016, 38(6):60-62, 72, 79
TAO Li, ZHU Xiaoguang, WANG Shanhong. Design of substation portable inspection terminal interface[J]. Huadian Technology, 2016, 38(6):60-62, 72, 79
[3] 史思总. 基于ROS的室内变电站设备巡检机器人定位技术[D]. 绵阳:西南科技大学, 2016.
SHI Sizong. Positioning technology of indoor substation equipment inspection robot based on ROS[D]. Mianyang, China:Southwest University of Science and Technology, 2016.
[4] 马一鸣. 智能巡检机器人在无人值守变电站的应用[D]. 北京:华北电力大学, 2017.
MA Yiming. The application of intelligent inspection robot in unattended substation[D]. Beijing:North China Electric Power University, 2017.
[5] ZHANG L Y, CHEN S F, DONG H W, et al. Visualizing Toronto city data with HoloLens:using augmented reality for a city model[J]. IEEE Consumer Electronics Magazine, 2018, 7(3):73-80.
[6] EL-HARIRI H, PANDEY P, HODGSON A J, et al. Augmented reality visualisation for orthopaedic surgical guidance with pre- and intra-operative multimodal image data fusion[J]. Healthcare Technology Letters, 2018, 5(5):189-193.
[7] KUHLEMANN I, KLEEMANN M, JAUER P, et al. Towards X-ray free endovascular interventions-using HoloLens for on-line holographic visualisation[J]. Healthcare Technology Letters, 2017, 4(5):184-187.
[8] FRANTZ T, JANSEN B, DUERINCK J, et al. Augmenting Microsoft's HoloLens with vuforia tracking for neuronavigation[J]. Healthcare Technology Letters, 2018, 5(5):221-225.
[9] XIAO R, SCHWARZ J, THROM N, et al. MRTouch:adding touch input to head-mounted mixed reality[J]. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(4):1653-1660.
[10] GRUBERT J, ITOH Y, MOSER K, et al. A survey of calibration methods for optical see-through head-mounted displays[J]. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(9):2649-2662.
[11] 崔巨勇, 曹云东, 王文杰. 基于分水岭与Krawtchouk不变矩相结合的改进方法在变电站巡检图像处理中的应用[J]. 中国电机工程学报, 2015, 35(6):1329-1335
CUI Juyong, CAO Yundong, WANG Wenjie. Application of an improved algorithm based on watershed combined with krawtchouk invariant moment in inspection image processing of substations[J]. Proceedings of the CSEE, 2015, 35(6):1329-1335
[12] COUPRIE C, GRADY L, NAJMAN L, et al. Power watershed:a unifying graph-based optimization framework[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(7):1384-1399.
[13] 原玥, 王宏, 原培新, 等. 一种改进的Hu不变矩算法在存储介质图像识别中的应用[J]. 仪器仪表学报, 2016, 37(5):1042-1048
YUAN Yue, WANG Hong, YUAN Peixin, et al. An improved Hu invariant moment algorithm for storage medium image recognition[J]. Chinese Journal of Scientific Instrument, 2016, 37(5):1042-1048
[14] 钱海, 贾松江, 杨飞, 等. 基于移动互联的继电保护设备智能运维技术研究[J]. 智慧电力, 2019, 47(11):60-66
QIAN Hai, JIA Songjiang, YANG Fei, et al. Intelligent operation and maintenance technology for relay protection equipment based on mobile Internet[J]. Smart Power, 2019, 47(11):60-66
[15] 杨春波, 陶青, 张健, 等. 基于综合健康指数的设备状态评估[J]. 电力系统保护与控制, 2019, 47(10):104-109
MARTIN D, CUI Y, EKANAYAKE C, et al. An updated model to determine the life remaining of transformer insulation[J]. IEEE Transactions on Power Delivery, 2019, 47(10):104-109
[16] 张慧源, 许力, 鲁二峰, 周峰. 基于混合粒子滤波的电力设备载流故障预测[J]. 仪器仪表学报, 2013, 34(6):222-228
ZHANG Huiyuan, XU Li, LU Erfeng, ZHOU Feng. Current-carrying fault prediction of electric equipment based on hybrid particle filtering[J]. Chinese Journal of Scientific Instrument, 2013, 34(6):222-228
[17] 黄欢, 雷加智, 曾华荣, 等. 极端外部环境下输电线路的综合风险评估方法[J]. 电力科学与技术学报, 2019, 34(2):119-127
HUANG Huan, LEI Jiazhi, ZENG Huarong, et al. Integrated risk assessment system of transmission line under extreme external environment[J]. Journal of Electric Power Science and Technology, 2019, 34(2):119-127
[18] 任腾云, 陈刚, 王春波, 等. 基于大数据的电力企业资产组维修策略优化研究[J]. 电力大数据, 2018, 21(12):12-19
REN Tengyun, CHEN Gang, WANG Chunbo, etal. Research on maintenance strategy optimization of power enterprise asset group based on big data[J]. Power Systems and Big Data, 2018, 21(12):12-19
[19] 冯玎, 林圣, 张奥, 等. 基于连续时间马尔可夫退化过程的牵引供电设备可靠性预测方法研究[J]. 中国电机工程学报, 2017, 37(7):1937-1946
FENG Ding, LIN Sheng, ZHANG Ao, et al. Research on reliability prediction method for traction power supply equipment based on continuous time Markov degradation process[J]. Proceedings of the CSEE, 2017, 37(7):1937-1946
[20] 李莉, 熊炜, 何杰, 等. 基于改进灰色马尔可夫模型的设备故障率预测[J]. 电力科学与工程, 2015, 31(8):20-24
LI Li, XIONG Wei, HE Jie, et al. Prediction of the equipment failure rate based on improved grey markov model[J]. Electric Power Science and Engineering, 2015, 31(8):20-24
[21] LUH P B, YU Y W, ZHANG B J, et al. Grid integration of intermittent wind generation:a Markovian approach[J]. IEEE Transactions on Smart Grid, 2014, 5(2):732-741.
[22] SLATKIN M. The dynamics of a population in a Markovian environment[J]. Ecology, 1978, 59(2):249-256.
[23] HACHTEL G D, MACII E, PARDO A, et al. et al. Markovian analysis of large finite state machines[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1996, 15(12):1479-1493.
[24] FALMAGNE J C, DOIGNON J P. A. Markovian procedure for assessing the state of a system[J]. Journal of Mathematical Psychology, 1988, 32(3):232-258.
[25] LIANG Z L, PARLIKAD A. A. Markovian model for power transformer maintenance[J]. International Journal of Electrical Power & Energy Systems, 2018, 99:175-182.