You are here

Home » Content

ESTIMATION OF THE CONDUCTOR AND TOWER CHARACTRISTICS USING NEURAL NETWORK

Journal Name:

  • Cumhuriyet Üniversitesi Fen Fakültesi Fen Bilimleri Dergisi

Publication Year:

  • 2015

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Neural networks are intelligent calculator tools which have the capability of learning and they are considered as a solution and a novel methodology in solving the unknown phenomena in the fields of science and technology. Neural networks learn the dynamic state behavior of a system using its performance or a phenomenon occurrence. Furthermore, they are able to simulate a system model or a complicated phenomenon with non-linear behavior even by using examples with high desperation and turbulence. Using advanced learning algorithms, neural networks will be converged towards the physic of the phenomenon or the system rapidly and accurately. In this survey, after presenting a concise explanation of biologic neurons, artificial neurons will be studied. Then, the structure and relations ruling the artificial neural networks as well as the learning algorithm will be surveyed. Consequently, the particular implementation of neural network in the problem of estimating the attributes of power network and the neural network success will be evaluated in this article by presenting examples.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon 5000129506-5000209238-1-pb.pdf
  • 6
39
49
  • English

REFERENCES

References: 

[1] Rafiq, M. Y., Bugmann, G., and Easterbrook, D.J. (2001). "Neural network design for
engineering applications." Computers & Structures 79(2): 1541-1552.
0 5 10 15 20 25 30 35 40 45 50
2
3
4
5
6
7
8
9
10
Number of Wires
0 5 10 15 20 25 30 35 40 45 50
-10
0
10
20
30
40
50
60
Bundle Diameter (cm)
Cuckoo Optimization Algorithm Based Design For Low-Speed Linear Induction Motor
49
[2] Beatty J. (2000). “The human brain: essentials of behavioral neuroscience”, California USA
Sage Publications, 2 (3): 101–135.
[3] Sadiku, M.N.O.(2004) . Numerical Technique in Electromagnetics. New York: CRC Press
[4] Sanger, M., Terence, D. (1989). "Optimal unsupervised learning in a single-layer linear
feedforward neural network." Neural networks 2(6): 459-473.
[5] Papagiannis, G.K., Tsiamitros, D.A., Labridis, D.P., Dokopoulos, P.S. (2005). Direct
numerical evaluation of earth path impedances of underground cables, IEE Proc.-Gener.
Transm. Distrib., 152(3):321–327.
[6] Papagiannis, G.K., Tsiamitros, D.A., Labridis, D.P., Dokopoulos, P.S., (2005). A systematic
approach to the evaluation of the influence of multilayered earth on overhead power
transmission lines, IEEE Trans. Power Del., 20(4):2594–2601.
[7] Tsiamitros, D.A., Papagiannis, G.K., Labridis, D.P., Dokopoulos, P.S. (2005). “Earth return
path impedance of underground cables for the two layer case”. IEEE Trans. Power
Del., 20(3):2174–2181.
[8] Carson, J.R. (1926). “Wave propagation in overhead wires with ground return.” Bell Syst.
Tech. J., . 5(3): 539–554.
[9] Xu, X.B., Liu, G., Chow, P. (2002). “A finite-element method solution of the zero-sequence
impedance of underground pipe-type cable”.IEEE Trans. Power Del., 17(1):13–17.
[10] Zhang, B., Cui, X., Li, L., He, J. (2005). “Parameter estimation of horizontal multilayer earth
by complex image method”. IEEE Trans. Power Del.,20(2):1394–1401.
[11] Uribe, J., Naredo, L., Moreno, P., and Guardado, L. (2004). “Algorithmic evaluation of
underground cable earth impedances.” IEEE Trans. Power Del., 19(1): 316–322,
[12] Cirino, André W., et al. (2009). "Cable parameter variation due to skin and proximity effects:
determination by means of finite element analysis." Industrial Electronics, 2009. IECON'09.
35th Annual Conference of IEEE. IEEE
[13] Habib, M.D. (2011). “Electromagnetic full wave modal analysis of frequency-dependent
underground cables”, Diss. University of Manitoba.
[14] Papagiannis, D. G. Triantafyllidis, and D. P. Labridis, “A one-step finite element formulation
for the modeling of single and double circuit transmission lines,” IEEE Trans. Power Syst.,
vol. 15, no. 1, pp. 33–38, Feb. 2000.

Thank you for copying data from http://www.arastirmax.com