Neural network method for predicting accidents due to formation of ice on power lines overhead
Keywords:
forecasting glaze load, neural network, meteopost, overhead power lineAbstract
The problem of prevention of faults caused by overhead power line glazing is considered. With this purpose, in the most fault-prone areas, automatic monitoring posts for glaze loads and meteorological parameters are installed the data of which are used to predict glazing. A neural network method for such prediction is proposed and has been tested on real data.References
1. Crocombette C. The weather impact on the transmission of electricity in France / C. Crocombette // Proc. 8th European conference on Application of Meteorology. — Madrid, Spain, 2007. — Vol. 4. — 670 p.
2. Makkonen L. Fifty years of progress in modeling the accumulation of atmospheric ice on power network equipment /
L. Makkonen, E. Lozowski // Proc. 11th International Workshop on Atmospheric Icing of Structures. — Montreal, Canada, 2005. — P. 55—62.
3. Estimation of transmission line icing at different sites using a neural network / P. Mc Comber, J. Druez, J. Lafontaine, A. Paradis, J. N. Laflamme // Proc. 9th International Offshore and Polar Engineering Conference. — Brest, France, 1999. —
Vol. II. — P. 599–606.
4. Jones K. F. A simple model for freezing rain ice loads / K. F. Jones // Atmospheric Research. — 1998. — Vol. 46. —
P. 87—97.
5. Makkonen L. Modeling power line icing in freezing precipitation / L. Makkonen // Atmospheric Research. — 1998. — Vol. 43. — P. 131—142.
6. Попов С. В. Еволюційна нейро-фаззі мережа на базі гібридних нейроподібних елементів / С. В. Попов, К.А. Шкуро // 17 міжнародна конференція з автоматичного управління «Автоматика—2010»: тези доповідей. Том 2. — Харків, 2010. — С. 193—194.
7. Попов С. В. Спеціалізована архітектура штучних нейронних мереж на базі гібридних нейроподібних елементів /
С. В. Попов // Збірник наукових праць Національного гірничого університету. — 2009. — Т. 2, № 33. — С. 76—82.
8. Haykin S. Neural Networks. A Comprehensive Foundation / S. Haykin. — Upper Saddle River: Prentice Hall, 1999. — 842 p.
9. Jain L. C. Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications / L. C. Jain,
N. M. Martin. — New York : CRC Press, 1998. — 368 p.
10. Schaffer J. D. Combinations of Genetic Algorithms and Neural Networks : A Survey of the State of the Art /
J. D. Schaffer, D. Whitley, L. J. Eshelman // Proc. Int. Workshop Combinations of Genetic Algorithms and Neural Networks. — Baltimore, June 6, 1992. — P. 1—37.
2. Makkonen L. Fifty years of progress in modeling the accumulation of atmospheric ice on power network equipment /
L. Makkonen, E. Lozowski // Proc. 11th International Workshop on Atmospheric Icing of Structures. — Montreal, Canada, 2005. — P. 55—62.
3. Estimation of transmission line icing at different sites using a neural network / P. Mc Comber, J. Druez, J. Lafontaine, A. Paradis, J. N. Laflamme // Proc. 9th International Offshore and Polar Engineering Conference. — Brest, France, 1999. —
Vol. II. — P. 599–606.
4. Jones K. F. A simple model for freezing rain ice loads / K. F. Jones // Atmospheric Research. — 1998. — Vol. 46. —
P. 87—97.
5. Makkonen L. Modeling power line icing in freezing precipitation / L. Makkonen // Atmospheric Research. — 1998. — Vol. 43. — P. 131—142.
6. Попов С. В. Еволюційна нейро-фаззі мережа на базі гібридних нейроподібних елементів / С. В. Попов, К.А. Шкуро // 17 міжнародна конференція з автоматичного управління «Автоматика—2010»: тези доповідей. Том 2. — Харків, 2010. — С. 193—194.
7. Попов С. В. Спеціалізована архітектура штучних нейронних мереж на базі гібридних нейроподібних елементів /
С. В. Попов // Збірник наукових праць Національного гірничого університету. — 2009. — Т. 2, № 33. — С. 76—82.
8. Haykin S. Neural Networks. A Comprehensive Foundation / S. Haykin. — Upper Saddle River: Prentice Hall, 1999. — 842 p.
9. Jain L. C. Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications / L. C. Jain,
N. M. Martin. — New York : CRC Press, 1998. — 368 p.
10. Schaffer J. D. Combinations of Genetic Algorithms and Neural Networks : A Survey of the State of the Art /
J. D. Schaffer, D. Whitley, L. J. Eshelman // Proc. Int. Workshop Combinations of Genetic Algorithms and Neural Networks. — Baltimore, June 6, 1992. — P. 1—37.
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Published
2010-11-12
How to Cite
[1]
S. V. Popov, N. M. Cheremysyn, O. V. Parkhomenko, and K. A. Shkuro, “Neural network method for predicting accidents due to formation of ice on power lines overhead”, Вісник ВПІ, no. 1, pp. 161–163, Nov. 2010.
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Energy generation and electrical engineering
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