Predicting the probability of ice storm damages to electricity transmission facilities based on ELM and Copula function

Neurocomputing

Volumn 74, Issue 16, 2011, Pages 2573-2581

  Yang, Hongming ^a   Xu, Wenjun ^a   Zhao, Junhua ^b   Wang, Dianhui ^c   Dong, Zhaoyang ^d  

^a CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c LA TROBE UNIVERSITY   (Australia)

^d UNIVERSITY OF NEWCASTLE   (Australia)

Author keywords

Copula function; Electricity transmission lines and towers; Extreme learning machine; Ice storms; Joint probability distribution

Indexed keywords

COPULA FUNCTIONS; ELECTRICITY TRANSMISSION; EXTREME LEARNING MACHINE; ICE STORMS; JOINT PROBABILITY DISTRIBUTION;

ELECTRIC LINES; ELECTRICITY; FORECASTING; LEARNING SYSTEMS; PRECIPITATION (METEOROLOGY); PROBABILITY DISTRIBUTIONS; STORMS; TOWERS; TRANSMISSION LINE THEORY;

ICE;

ARTICLE; COPULA FUNCTION; CORRELATION ANALYSIS; ELECTRICAL EQUIPMENT; ENVIRONMENTAL PARAMETERS; EXTREME LEARNING MACHINE; HAZARD ASSESSMENT; ICE LOAD; ICE STORM; LEARNING ALGORITHM; NATURAL DISASTER; PRIORITY JOURNAL; PROBABILITY; STATISTICAL ANALYSIS; STATISTICAL MODEL; WIND LOAD;

EID: 80051596782     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.12.039     Document Type: Article

References (27)

1. Liu H.B., Davidson R.A., Apanasovich T.V. Spatial generalized linear mixed models of electric power outages due to hurricanes and ice storms. Reliability Engineering and System Safety 2008, 93:875-890.
2. E. Brostrom, J. Ahlberg, L. Soder, Modelling of ice storms and their impact applied to a part of the Swedish transmission network, in: Power Technology Conference, Lausanne, Switzerland, July 2007, pp. 1593-1598.
3. Farzaneh M., Savadjiev K. Statistical analysis of field data for precipitation icing accretion on overhead power lines. IEEE Transactions on Power Delivery 2005, 20:1080-1087.
4. Krishnasamy S.G. Assessment of weather induce transmission line loads on a probabilistic basis. IEEE Transactions on Power Apparatus and Systems 1985, 104:2510-2516.
5. Huang G.B., Chen L., Siew C.-K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 2006, 17:879-892.
6. Huang G.B., Chen L. Convex incremental extreme learning machine. Neurocomputing 2007, 70:3056-3062.
7. Huang G.B., Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing 2008, 71:3460-3468.
8. Liang N.Y., Huang G.B., Saratchandran P., Sundararajan N. A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 2006, 17(6):1411-1423.
9. Feng G., Huang G.B., Lin Q., Gay R. Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks 2009, 20(8):1352-1357.
10. Huang G.B., Zhu Q.-Y., Mao K.Z., Siew C.-K., Saratchandran P., Sundararajan N. Can threshold networks be trained directly?. IEEE Transactions on Circuits and Systems-II 2006, 53(3):187-191.
11. Liang N.Y., Saratchandran P., Huang G.B., Sundararajan N. Classification of mental tasks from EEG signals using extreme learning machines. International Journal of Neural Systems 2006, 16(1):29-38.
12. Yeu C.W.T., Lim M.-H., Huang G.-B., Agarwal A., Ong Y.S. A new machine learning paradigm for terrain reconstruction. IEEE Geoscience and Remote Sensing Letters 2006, 3(3):382-386.
13. Zhang R., Huang G.-B., Sundararajan N., Saratchandran P. Multi-category classification using extreme learning machine for microarray gene expression cancer diagnosis. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2007, 4(3):485-495.
14. Kudzys A. Safety of power transmission line structures under wind and ice storms. Engineering Structures 2005, 28:682-689.
15. T.L.E. Brostrom, D.L. Soder, Ice storm impact on power system reliability, in: Proceedings of the 12th International Workshop on Atmospheric Icing on Structures (IWAIS2007), Yokohama, Japan, October 2007.
16. Huang G.B., Zhu Q.Y., Siew C.K. Extreme learning machine: theory and applications. Neurocomputing 2006, 70:489-501.
17. Mrazova I., Wang D.H. Improved generalization of neural classifiers with enforced internal representation. Neurocomputing 2007, 70:2940-2952.
18. Huang G.B., Li M.B., Chen L., Siew C.K. Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing 2008, 71:576-583.
19. Lan Y., Soh Y.C., Huang G.B. Ensemble of online sequential extreme learning machine. Neurocomputing 2009, 72:3391-3395.
20. Malathi V., Marimuthu N.S., Baskar S. Intelligent approaches using support vector machine and extreme learning machine for transmission line protection. Neurocomputing 2010, 73:2160-2167.
21. Han F., Huang D.S. Improved extreme learning machine for function approximation by encoding a priori information. Neurocomputing 2006, 69:2369-2373.
22. Sun Z.L., Tsan M.C., Kin F.A., Yong Y. Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems 2008, 46:411-419.
23. G.B. Huang, C.K. Siew, Extreme learning machine: RBF network case, in: Proceedings of the English International Conference on Control, Automation, Robotics and Vision, December 2004, pp. 1029-1036.
24. Ministry of Development of the People's Republic of China: GBJ 135-90 Design of high-rise structure, Beijing, China, 1991 (in Chinese).
25. Hofert M. Sampling archimedean copulas. Computational Statistics and Data Analysis 2008, 52:5163-5174.
26. Yu L., Voit E.O. Construction of bivariate S-distributions with copulas. Computational Statistics and Data Analysis 2005, 51:1822-1839.
27. Wang X., Makis V. Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov-Smirnov test. Sound and Vibration 2009, 327:413-423.