| Citation: | XU Dan, LI You, LI Helonget al. Operating status evaluation of smart electricity meters based on joint distribution adaption[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(4): 674-681. doi: 10.13700/j.bh.1001-5965.2020.0621(in Chinese)
|
In the operation status evaluation of smart electricity meters, it is often difficult to collect labeled data and the data distribution in different regions is inconsistent. To solve this problem, we introduce the joint distribution adaption (JDA) method in transfer learning in the field of operation status evaluation of smart electricity meters. This method tries to find an optimized transformation matrix to minimize the distance between edge distribution and conditional distribution in different regions in the transformed space. In order to solve the problem that there is no data label in the target domain when the conditional distribution adaptation is made, the JDA method uses pseudo-tag iteration method to make the target domain pseudo-label constantly approach the real tag. The classification model trained from the data in the post-transformation space can be applied to the new region to realize the transfer. The experimental results demonstrate the effectiveness of JDA method in the operation status evaluation of smart electricity meters.
| [1] |
陈文娴. 基于深度学习的小样本异常用电数据检测技术研究[D]. 武汉: 华中科技大学, 2018.
CHEN W X. Abnormal electricity utilization detection based on deep learning and few-shot learning[D]. Wuhan: Huazhong University of Science and Technology, 2018(in Chinese).
|
| [2] |
商曦文, 张颖, 吉莹, 等. 智能电表运行状态评估技术研究[J]. 自动化与仪器仪表, 2018, 11(3): 139-146.
SHANG X W, ZHANG Y, JI Y, et. al. Research on smart meters' running state assessment techniques[J]. Automation & Instrumentation, 2018, 11(3): 139-146(in Chinese).
|
| [3] |
CAI H, CHEN H Q, YE X Q, et al. An on-line state evaluation method of smart meters based on information fusion[J]. IEEE Access, 2019, 1(7): 163665-163676.
|
| [4] |
LI Y F, WU H, PANG S, et al. Application of supervised machine learning algorithms in diagnosis of abnormal voltage[J]. Electrical Measurement and Instrumentation, 2016, 53(1): 58-62.
|
| [5] |
叶剑斌, 朱东升, 汪翊节, 等. 智能电能表状态检验技术研究[J]. 自动化仪表, 2020, 41(1): 55-59. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDYB202001010.htm
YE J B, ZHU D S, WANG Y J, et al. Research on intelligent electric energy meter state inspection technology[J]. Process Aotumation Instrumentation, 2020, 41(1): 55-59(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZDYB202001010.htm
|
| [6] |
祝宇楠, 徐晴, 刘建, 等. 数据挖掘在智能电能表故障分析中的应用[J]. 电力工程技术, 2016, 35(5): 19-23. doi: 10.3969/j.issn.1009-0665.2016.05.005
ZHU Y N, XU Q, LIU J, et al. Application of data mining in fault analysis of intelligent electricity meters[J]. Power Engineering Technology, 2016, 35(5): 19-23(in Chinese). doi: 10.3969/j.issn.1009-0665.2016.05.005
|
| [7] |
韩笑. 基于不确定性信息融合的智能电能表状态评价方法研究[D]. 长春: 吉林大学, 2018: 22-58.
HAN X. Research on state evaluation method of smart electricity meter based on uncertainty information fusion[D]. Changchun: Jilin University, 2018: 22-58(in Chinese).
|
| [8] |
CHENG Y Y, HOU X Z, XIAO J. Design and realization of condition management system of gateway electrical[J]. Electrical Measurement and Instrumentation, 2013, 50(8): 87-92.
|
| [9] |
SHEN X, ZHAO D N, CAO M. Research on energy metering device remote online monitoring and analysis platform based on internet of thing[J]. Electrical Measurement and Instrumentation, 2015, 52(1): 35-40.
|
| [10] |
王新刚, 吴颖, 张垠. 基于数据挖掘的智能电能表在线监测方法[J]. 电测与仪表, 2016, 53(13): 65-69. doi: 10.3969/j.issn.1001-1390.2016.13.013
WANG X G, WU Y, ZHANG Y. Online monitoring method of intelligent electricity meters based on data mining[J]. Electric Measurement and Instrumentation, 2016, 53(13): 65-69(in Chinese). doi: 10.3969/j.issn.1001-1390.2016.13.013
|
| [11] |
LONG M S, WANG J, DING G, et al. Transfer feature learning with joint distribution adaptation[C]//2013 IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2013: 1-8.
|
| [12] |
CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: Synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2011, 16(1): 321-357.
|
| [13] |
BORGWARDT K M, GRETTON A, RASCH M J, et al. Integrating structured biological data by kernel maximum mean discrepancy[J]. Bioinformatics, 2006, 22(14): 49-57. doi: 10.1093/bioinformatics/btl242
|
| [14] |
FILISBINO T A, GIRALDI G A, THOMAZ C E. Support vector machine ensembles for discriminant analysis for ranking principal components[J]. Multimedia Tools and Applications, 2020, 79(5): 1-37.
|
| [15] |
PAN S J, TSANG I W, KWOK J T, et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks, 2011, 22(2): 199-210. doi: 10.1109/TNN.2010.2091281
|
| [16] |
GRETTON A, BORGWARDT K M, RASCH M J, et al. A kernel method for the two-sample problem[J]. Neural Information Processing Systems, 2006, 4(3): 1-10.
|
| [17] |
FAN J, KE Z T, LIU H, et al. QUADRO: A supervised dimension reduction method via Rayleigh quotient optimization[J]. Annals of Statistics, 2015, 4(43): 1498-1534.
|
| [18] |
龙明盛. 迁移学习问题与方法研究[D]. 北京: 清华大学, 2014.
LONG M S. Research on transfer learning problems and methods[D]. Beijing: Tsinghua University, 2014(in Chinese).
|
Figures(3) / Tables(6)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
