Intelligent system to control electric power distribution networks
Bilah Samping Artikel
ISSN online:
2255-2863
Prefijo Doi:
10.14201
Isi Artikel Utama
Vol. 4 No. 4 (2015), Articles, pages 1-8
Abstract
The use of high voltage power lines transport involves some risks that may be avoided with periodic reviews as imposed by law in most countries. The objective of this work is to reduce the number of these periodic reviews so that the maintenance cost of power lines is also reduced. To reduce the number of transmission towers (TT) to be reviewed, a virtual organization (VO) based system of agents is proposed in conjunction with different artificial intelligence methods and algorithms. This system is able to propose a sample of TT from a selected set to be reviewed and to ensure that the whole set will have similar values without needing to review all the TT. As a result, the system provides a software solution to manage all the review processes and all the TT of Spain, allowing the review companies to use the application either when they initiate a new review process for a whole line or area of TT, or when they want to place an entirely new set of TT, in which case the system would recommend the best place and the best type of structure to use.
Keywords:
Transmission Towers, Agents, Electric Power Distribution, CBR, ANN
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References
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Eltawil, M. A., & Zhao, Z. (2010). Grid-connected photovoltaic power systems: Technical and potential pro-blems—A review. Renewable and Sustainable Energy Reviews, 14(1), 112-129.
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Gonçalves, R. S., & Carvalho, J. C. M. (2013). Review and Latest Trends in Mobile Robots Used on Power Transmission Lines. International Journal of Advanced Robotic Systems (Print), 10, 1-14.
Hennig, C. and Liao, T. (2013) How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification, Journal of the Royal Statistical Society, Series C Applied Statistics, 62, 309-369.
http://dx.doi.org/10.1111/j.1467-9876.2012.01066.x
Ji, K., Rui, X., Li, L., Leblond, A., & McClure, G. (2015). A novel ice-shedding model for overhead power line conductors with the consideration of adhesive/cohesive forces. Computers & Structures, 157, 153-164.
http://dx.doi.org/10.1016/j.compstruc.2015.05.014
Krishnanand, K. R., Dash, P. K., & Naeem, M. H. (2015). Detection, classification, and location of faults in power transmission lines. International Journal of Electrical Power & Energy Systems, 67, 76-86.
http://dx.doi.org/10.1016/j.ijepes.2014.11.012
Na, M. G. (2001). Auto-tuned PID controller using a model predictive control method for the steam generator water level. Nuclear Science, IEEE Transactions on, 48(5), 1664-1671.
http://dx.doi.org/10.1109/23.960354
Singh, J., Gandhi, K., Kapoor, M., & Dwivedi, A (2013). New Approaches for Live Wire Maintenance of Trans-mission Lines.
Smith, C. A., Corripio, A. B., & Basurto, S. D. M. (1991). Control automático de procesos: teoría y práctica. Li-musa.
Taher, S. A., & Sadeghkhani, I. (2010). Estimation of magnitude and time duration of temporary overvoltages using ANN in transmission lines during power system restoration. Simulation Modelling Practice and Theory, 18(6), 787-805.
http://dx.doi.org/10.1016/j.simpat.2010.01.016
Trappey, A. J., Trappey, C. V., Ma, L., & Chang, J. C. (2015). Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions. Computers & Industrial Engineering, 84, 3-11.
http://dx.doi.org/10.1016/j.cie.2014.12.033
Weibull, W. (1951). Wide applicability. Journal of applied mechanics, 103, 33.
Zarnani, A., Musilek, P., Shi, X., Ke, X., He, H., & Greiner, R. (2012). Learning to predict ice accretion on electric power lines. Engineering Applications of Artificial Intelligence, 25(3), 609-617.
http://dx.doi.org/10.1016/j.engappai.2011.11.004
Zhou, D., Zhang, H., & Weng, S. (2014). A novel prognostic model of performance degradation trend for power machinery maintenance. Energy, 78, 740-746.
http://dx.doi.org/10.1016/j.energy.2014.10.067