Intelligent Control of Energy Distribution Networks

  • Pablo Chamoso
  • Juan Francisco De Paz
  • Javier Bajo
  • Gabriel Villarrubia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 473)


There has been continuous research in the energy distribution sector over the last years because of its significant impact in modern societies. Nonetheless, the use of high voltage power lines transport involves some risks that may be avoided with periodic reviews. 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. This work is focused on the periodic review of transmission towers (TT) to avoid important risks, such as step and touch potentials, for humans. To reduce the number of TT to be reviewed, an organization-based agent system 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 web application 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.


Power lines management Intelligent systems Agents Virtual organization Data analysis Case based reasoning Artificial neural networks 


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  1. 1.
    de Faria, H., Costa, J.G.S., Olivas, J.L.M.: A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis. Renewable and Sustainable Energy Reviews 46, 201–209 (2015)CrossRefGoogle Scholar
  2. 2.
    Duval, M., DePabla, A.: Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases. Electrical Insulation Magazine 17(2), 31–41 (2001). IEEECrossRefGoogle Scholar
  3. 3.
    Eltawil, M.A., Zhao, Z.: Grid-connected photovoltaic power systems: Technical and potential problems—A review. Renewable and Sustainable Energy Reviews 14(1), 112–129 (2010)CrossRefGoogle Scholar
  4. 4.
    Gonçalves, R.S., Carvalho, J.C.M.: Review and Latest Trends in Mobile Robots Used on Power Transmission Lines. International Journal of Advanced Robotic Systems (Print) 10, 1–14 (2013)Google Scholar
  5. 5.
    Hennig, C., Liao, T.: 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 (2013)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Ji, K., Rui, X., Li, L., Leblond, A., McClure, G.: A novel ice-shedding model for overhead power line conductors with the consideration of adhesive/cohesive forces. Computers & Structures 157, 153–164 (2015)CrossRefGoogle Scholar
  7. 7.
    Krishnanand, K.R., Dash, P.K., Naeem, M.H.: Detection, classification, and location of faults in power transmission lines. International Journal of Electrical Power & Energy Systems 67, 76–86 (2015)CrossRefGoogle Scholar
  8. 8.
    Na, M.G.: Auto-tuned PID controller using a model predictive control method for the steam generator water level. IEEE Transactions on Nuclear Science 48(5), 1664–1671 (2001)CrossRefGoogle Scholar
  9. 9.
    Singh, J., Gandhi, K., Kapoor, M., Dwivedi, A.: New Approaches for Live Wire Maintenance of Transmission LinesGoogle Scholar
  10. 10.
    Smith, C.A., Corripio, A.B., Basurto, S.D.M.: Control automático de procesos: teoría y práctica. Limusa (1991)Google Scholar
  11. 11.
    Taher, S.A., Sadeghkhani, I.: 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 (2010)CrossRefGoogle Scholar
  12. 12.
    Trappey, A.J., Trappey, C.V., Ma, L., Chang, J.C.: Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions. Computers & Industrial Engineering 84, 3–11 (2015)CrossRefGoogle Scholar
  13. 13.
    Weibull, W.: Wide applicability. Journal of Applied Mechanics 103, 33 (1951)zbMATHGoogle Scholar
  14. 14.
    Zarnani, A., Musilek, P., Shi, X., Ke, X., He, H., Greiner, R.: Learning to predict ice accretion on electric power lines. Engineering Applications of Artificial Intelligence 25(3), 609–617 (2012)CrossRefGoogle Scholar
  15. 15.
    Zhou, D., Zhang, H., Weng, S.: A novel prognostic model of performance degradation trend for power machinery maintenance. Energy 78, 740–746 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Pablo Chamoso
    • 1
  • Juan Francisco De Paz
    • 1
  • Javier Bajo
    • 2
  • Gabriel Villarrubia
    • 1
  1. 1.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain
  2. 2.Department of Artificial IntelligencePolytechnic University of MadridMadridSpain

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