Design of an AI-Based Workflow-Guiding System for Stratified Sampling

  • G. HernándezEmail author
  • D. García-Retuerta
  • P. Chamoso
  • A. Rivas
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1006)


The characterization of the resistance of transmission towers is a difficult and costly procedure which can be mitigated using statistical techniques. A stratified sampling process based on the characteristic of the terrain was shown in previous works to reduce the error in the statistical inference; however, such characteristics are usually unknown before a measure is made. In this work, we present a system which integrates artificial intelligence techniques, such as k-nearest neighbors, decision trees, or random forests, to automatically optimize the workflow of expert workers using various sources of data.


Ambient artificial intelligence Statistical sampling Transmission towers 



This research has been partially supported by the European Regional Development Fund (FEDER) within the framework of the Interreg program V-A Spain-Portugal 2014–2020 (PocTep) under the IOTEC project grant 0123 IOTEC 3 E.


  1. 1.
    Singh, J., Gandhi, K., Kapoor, M., Dwivedi, A.: New approaches for live wire maintenance of transmission lines. MIT Int. J. Electr. Instrum. Eng. 3(2), 67–71 (2013)Google Scholar
  2. 2.
    Gonçalves, R.S., Carvalho, J.C.M.: Review and latest trends in mobile robots used on power transmission lines. Int. J. Adv. Rob. Syst. 10(12), 408 (2013)CrossRefGoogle Scholar
  3. 3.
    Eltawil, M.A., Zhao, Z.: Grid-connected photovoltaic power systems: technical and potential problems—a review. Renew. Sustain. Energy Rev. 14(1), 112–129 (2010)CrossRefGoogle Scholar
  4. 4.
    Swanson, L.: Linking maintenance strategies to performance. Int. J. Prod. Econ. 70(3), 237–244 (2001)CrossRefGoogle Scholar
  5. 5.
    Ghazvini, M.A.F., Morais, H., Vale, Z.: Coordination between mid-term maintenance outage decisions and short-term security-constrained scheduling in smart distribution systems. Appl. Energy 96, 281–291 (2012)CrossRefGoogle Scholar
  6. 6.
    Smith, C.A., Corripio, A.B., Basurto, S.D.M.: Control automático de procesos: teoría y práctica. Number 968-18-3791-6. 01-A3 LU. AL-PCS. 1. Limusa (1991)Google Scholar
  7. 7.
    Na, M.G.: Auto-tuned PID controller using a model predictive control method for the steam generator water level. IEEE Trans. Nucl. Sci. 48(5), 1664–1671 (2001)CrossRefGoogle Scholar
  8. 8.
    Krishnanand, K.R., Dash, P.K., Naeem, M.H.: Detection, classification, and location of faults in power transmission lines. Int. J. Electr. Power Energy Syst. 67, 76–86 (2015)CrossRefGoogle Scholar
  9. 9.
    Taher, S.A., Sadeghkhani, I.: Estimation of magnitude and time duration of temporary overvoltages using ANN in transmission lines during power system restoration. Simul. Model. Pract. Theory 18(6), 787–805 (2010)CrossRefGoogle Scholar
  10. 10.
    Higgins, L.R., Mobley, R.K., Smith, R., et al.: Maintenance Engineering Handbook. McGraw-Hill, New York (2002)Google Scholar
  11. 11.
    Do, P., Voisin, A., Levrat, E., Iung, B.: A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions. Reliab. Eng. Syst. Saf. 133, 22–32 (2015)CrossRefGoogle Scholar
  12. 12.
    Zarnani, A., Musilek, P., Shi, X., Ke, X., He, H., Greiner, R.: Learning to predict ice accretion on electric power lines. Eng. Appl. Artif. Intell. 25(3), 609–617 (2012)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    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. Renew. Sustain. Energy Rev. 46, 201–209 (2015)CrossRefGoogle Scholar
  15. 15.
    Trappey, A.J.C., Trappey, C.V., Ma, L., Chang, J.C.M.: Intelligent engineering asset management system for power transformer maintenance decision supports under various operating conditions. Comput. Ind. Eng. 84, 3–11 (2015)CrossRefGoogle Scholar
  16. 16.
    Weibull, W.: Wide applicability. Int. J. Appl. Mech. 103(730), 293–297 (1951)zbMATHGoogle Scholar
  17. 17.
    Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods, vol. 751. Wiley, Hoboken (2013)zbMATHGoogle Scholar
  18. 18.
    Chakraborti, S., Li, J.: Confidence interval estimation of a normal percentile. Am. Stat. 61(4), 331–336 (2007)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Chamoso, P., De La Prieta, F., Villarrubia, G.: Intelligent system to control electric power distribution networks. DCAIJ Adv. Distrib. Comput. Artif. Intell. J. 4(4), 1–8 (2015)Google Scholar
  20. 20.
    Chamoso, P., De Paz, J.F., Bajo, J., Villarrubia, G.: Intelligent control of energy distribution networks. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 99–107. Springer (2016)Google Scholar
  21. 21.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • G. Hernández
    • 1
    Email author
  • D. García-Retuerta
    • 1
  • P. Chamoso
    • 1
  • A. Rivas
    • 1
  1. 1.Bisite Research GroupUniversidad de SalamancaSalamancaSpain

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