The mining of high risk equipment based on the algorithm of HR-tree’s decision
Wang Shanyuan 1  
,  
Zhang Yujie 2  
,  
Li Yao 2
,  
Gao Suisui 2
,  
 
 
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1
Northeastern University,College Of Information Science and Engineering, 110004
2
Northeastern University, College of Information Science and Engineering, Institute of Electrical Automation, 110004
CORRESPONDING AUTHOR
Wang Shanyuan   

Northeastern University,College Of Information Science and Engineering, 110004
Online publication date: 2020-05-13
Publication date: 2020-05-13
Submission date: 2020-03-13
Final revision date: 2020-05-06
Acceptance date: 2020-05-12
 
Diagnostyka 2020;21(2):51–59
KEYWORDS
TOPICS
ABSTRACT
Due to the different construction of various subsystems in the power grid, the information of various systems are not closely connected.Nowadays,the network is complex and changeable where the automation is getting higher. This article takes high-risk equipment set of substation in Liaoyang as the research background. It constructs HR-Tree for the device set, and establishes a high-risk equipment evaluation system which based on the HR-Tree context. Then we calculate high-risk equipment sets in the structure of overall data set.By establishing the original data set and the prior knowledge system of equipment risk, the non-candidate high-risk equipment set is reduced in the local path of the high-risk equipment set.We refer to the process of reducing data as minus branch. After the threshold is established, the branches are reduced and the highest risk equipment set is obtained. Finally, we use the scoring system to find the probability of occurrence of associated devices, such information is more open.Example showed that such methods could effectively express high-risk device sets, and managers could get early warning information based on this. It helps people monitoring the power system,which could also provides new ideas for the monitoring project.
 
REFERENCES (19)
1.
Li G, Tang J. A new HR-tree index based on hash address[C]. 2010 2nd International Conference on Signal Processing Systems.2010;pp. V3-35-V3-38. https://doi.org/10.1109/ICSPS.....
 
2.
Xing Yu. research on power transformer fault diagnosis technology based on fault tree and signal analysis. Harbin Institute of Technology. 2017.
 
3.
Ren Dongmei, Zhang Yuyang, DONG Xinling. Application of improved principal component analysis-Bayes discriminant analysis method to petroleum drilling safety evaluation. Journal of Computer Applications.2017,37(06):1820-1824. https://doi.org/10.11772/j.iss....
 
4.
Yu H, Wu ZR, Bao T F. Multivariate analysis in dam monitoring data with PCA. Science China Technological Sciences. 2010;53(4):1088-1097. https://doi.org/10.1007/s11431....
 
5.
Lanzrath M, Suhrke M Hirsch H. HPEM-based risk assessment of substations enabled for the smart grid. IEEE Transactions on Electromagnetic Compatibility. 2020;62(8):173-185. https://doi.org/10.1109/TEMC.2....
 
6.
Karaolis MA, Moutiris JA, Hadjipanayi D, Pattichis CS. Assessment of the risk factors of coronary heart events based on data mining with decision trees. IEEE Transactions on Information Technology in Biomedicine.2010;14(3):559-566. https://doi.org/10.1109/TITB.2....
 
7.
Samiri MY, Najib M, Elfazziki A, Abourraja MN, Boudebous D, Bouain A. APRICOIN: An adaptive approach for prioritizing high-risk containers inspections. IEEE Access.2017;9(5):18238-18249. https://doi.org/10.1109/ACCESS....
 
8.
Lee, Sang-Woo, Kwon, Jung-Hyok, Lee, Ben, Kim, Eui-Jik. Scientific literature information extraction using text mining techniques for human health risk assessment of electromagnetic fields. Sensors and Materials.2020;32(1): 149-157. https://doi.org/10.18494/SAM.2....
 
9.
W. Gan, JC Lin, P. Fournier-Viger, H. Chao and P. S. Yu. HUOPM: High-Utility Occupancy Pattern Mining. IEEE Transactions on Cybernetics. 2020;50(3):1195-1208. https://doi.org/10.1109/TCYB.2....
 
10.
Ma Lijie, Zhlj Yongli, Zheng Yanyan. Research on transformer fault diagnosis and optimization based on parallel variable prediction model. Power System Protection and Control. 2019,47(6):82-89. https://doi.org/10.7667/PSPC18....
 
11.
Zhang Xuewei, Li Hanshan. Research on transformer fault diagnosis method and calculation model by using fuzzy data fusion in multi-sensor detection system. Optik. 2019;176(9):716-723. https://doi.org/10.1016/j.ijle....
 
12.
Mou Shanzhong, Xu Tianci, Fu Ao, Wang MenG, Bai Ru. Fault diagnosis method of transformer based on adaptive deep learning model. Southern Power System Technology. 2018;12(10):14-19. https://doi.org/10.13648/j.cnk....
 
13.
LiLi Mo. Transformer fault diagnosis method based on support vector machine and ant colony. Advanced Materials Research. 2013;659(1):54-58. https://doi.org/10.4028/www.sc....
 
14.
Li Wenfeng, You Qinghe, Liao Qiang. Research on remote fault diagnosis method based on T-S fuzzy FTA. Control Engineering of China.2018,25(9):1703-1708. https://doi.org/10.14107/j.cnk....
 
15.
Chaolong Zhang, Yigang He, Bolun Du, Lifen Yuan, Bing Li, Shanhe Jiang. Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning. Future Generation Computer Systems.2020,108(3): 533-545. https://doi.org/10.1016/j.futu....
 
16.
Arturo Mejia-Barron, Martin Valtierra-Rodriguez, David Granados-Lieberman, Juan C. Olivares-Galvan, Rafael Escarela-Perez. The application of EMD-based methods for diagnosis of winding faults in a transformer using transient and steady state currents. Measurement. 2018; 117(12): 371-379. https://doi.org/10.1016/j.meas....
 
17.
Xu Jinyong, Luo Shijun, Zhang Zida. Fault diagnosis system of wheel loader hydraulic system based on fuzzy fault tree analysis. Journal of Jilin University (Engineering and Technology Edition). 2007;37(3):569-574. https://doi.org/10.13229/j.cnk....
 
18.
Hu Jun, Yin Liqun, Li Zhen, Guo Lijuan, Duan Lian, Zhang Yubo. Fault diagnosis method of transmission and transformation equipment based on big data mining technology. High Voltage Engineering.2017;43(11):3690-3697. https://doi.org/10.13336/j.100....
 
19.
Wang Tao, Sun Zhipeng, Cui Qing, Zhang Zhilei, Zhang Tianwei. Research on fault diagnosis of power transformer based on classification decision tree algorithm. Electrical Engineering. 2019;20(11):16-19.
 
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