Decision-Making Support Method for the Preventive Substitution of Surge Arresters on Distribution Systems

  • Marcel A. Araújo
  • Danilo H. Spatti
  • Luisa H. B. Liboni
  • Luiz A. Pergentino
  • Fabricio E. Viana
  • Rogério A. Flauzino


In the protection systems of power distribution networks, surge arresters are critical for protecting the grid against overvoltages caused by lightning. This equipment is subject to high voltages and currents during its operation, which degrades its expected lifetime. In general, the replacement of surge arresters is based on corrective maintenance procedures, which exposes the electrical system to failures since the protective characteristics of the arresters have already degraded. In this context, we seek to develop a new method for estimating the life span of surge arresters and a set of criteria for their preventive replacement. The method consists of assessing the correlation between fault occurrences in a distribution system with the occurrence of lightning in the concession area of a power utility company. In addition, the method assesses the correlation between the fault and lightning occurrences and data related to the degradation of such devices obtained by experimental and field tests in new surge arresters to estimate their life span and to implement a decision-making support system for their preventive substitution.


Lightning Overvoltage protection Surge arrester Preventive maintenance Distribution system 



The authors thank the ANEEL R&D Program, Contract Number PD-0391-0020/2016.


  1. Abdul-Malek, Z., Khavari, A. H., Wooi, C. L., Moradi, M., & Naderipour, A. (2015). A review of modeling ageing behavior and condition monitoring of zinc Oxide Surge Arrester. In IEEE Student Conference on Research and Development (SCOReD) (pp. 733–738).Google Scholar
  2. Araujo, M. A., Flauzino, R. A., Altafim, R. A. C., Batista, O. E., & Moraes, L. A. (2015). Practical methodology for modeling and simulation of a lightning protection system using metal-oxide surge arresters for distribution lines. Electric Power Systems Research, 118, 47–54.CrossRefGoogle Scholar
  3. Ariffin, M. (2009). Challenges in developing surge arrester failure detection methodologies in TNB distribution network. In CIRED 200920th international conference and exhibition on electricity distribution.Google Scholar
  4. Bassi, W., & Tatizawa, H. (2016). Early prediction of surge arrester failures by dielectric characterisation. IEEE Electrical Insulation Magazine, 32(2), 35–44.CrossRefGoogle Scholar
  5. Christodoulou, C. A., Ekonomou, L., Fotis, G. P., Gonos, I. F., & Stathopulos, I. A. (2010). Assessment of surge arrester failure rate and application studies in hellenic high voltage transmission lines. Electric Power Systems Research, 80(2), 176–183.CrossRefGoogle Scholar
  6. Christodoulou, C. A., Perantzakis, G., Spanakis, G. E., & Karampelas, P. (2012). Evaluation of lightning performance of transmission lines protected by metal oxide surge arresters using artificial intelligence techniques. Energy Systems, 3(4), 379–399.CrossRefGoogle Scholar
  7. Cooray, V. (2009). Lightning protection (p. 1072). IET Digital Library.Google Scholar
  8. Ferreira, C. A., Coser, E. J., Aangelini, J. M. G., Rossi, J. A. D., & Martinez, J. A. D. (2011). Effect of artificial aging on polymeric surge arresters and polymer insulators for electricity distribution networks. Polímeros, 21(5), 436–442.CrossRefGoogle Scholar
  9. Goh, H. H., Sim, S., Shaari, J., Azali, N. A., Ling, C. W., Chua, Q. S., et al. (2017). A review of lightning protection system—risk assessment and application. Indonesian Journal of Electrical Engineering and Computer Science, 8(1), 221–229.Google Scholar
  10. Hall, M. A., & Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering, 15(3), 1–16.Google Scholar
  11. Haykin, S. (2008). Neural networks and learning machines (3rd ed.). New Jersey: Editora Prentice-Hall.Google Scholar
  12. He, J., Lin, L., Zhang, L., Liu, W., & Li, S. (2017). Indication of initial degradation of ZnO varistor ceramics Jinqiang. In 1st International Conference on Electrical Materials and Power Equipment (ICEMPE) (pp. 529–532).Google Scholar
  13. Hernandez, R., Ramirez, I., Saldivar, R., & Montoya, G. (2012). Analysis of accelerated ageing of non-ceramic insulation equipments. IET Generation, Transmission and Distribution, 6(1), 59–68.CrossRefGoogle Scholar
  14. Khodsuz, M., Mirzaie, M., & Seyyedbarzegar, S. (2015). Metal oxide surge arrester condition monitoring based on analysis of leakage current components. International Journal of Electrical Power & Energy Systems, 66, 188–193.CrossRefGoogle Scholar
  15. Li, S., Li, J., Liu, W., Lin, J., He, J., & Cheng, P. (2015). Advances in ZnO varistors in China during the past 30 years—fundamentals, processing, and applications. IEEE Electrical Insulation Magazine, 31(4), 35–44.CrossRefGoogle Scholar
  16. Likitha S., Kanyakumari M., Jithin Pauly P., Shivakumara Aradhya R. S., & Vasudev N. (2016). Estimation of critical resistive leakage current of polymer housed ZnO surge arrester by electro-thermal modelling. Journal of Electrical Systems and Information Technology, In press, corrected proof, Available online December 28, 2016.Google Scholar
  17. Liljestrand, L., & Lindell, E. (2016). Efficiency of surge arresters as protective devices against circuit-breaker induced overvoltages. IEEE Transactions on Power Delivery, 31(4), 1562–1570.CrossRefGoogle Scholar
  18. Mamede, J. F. (2005). Manual de Equipamentos Elétricos (3rd ed., Vol. 1). Rio de Janeiro: Ed. Livros Técnicos e Científicos.Google Scholar
  19. Medeiros, R. A. C., Freire, R. C. S., Da Costa, E. G., De Lira, G. R., Neto, E. T. W., & Maia, M. (2009). Monitoramento e diagnóstico de pára-raios a ZnO usando redes neurais artificiais. In VIII SEMETRO.Google Scholar
  20. Metwally, I. A., Eladawy, M., & Feilat, E. A. (2017). Online condition monitoring of surge arresters based on third-harmonic analysis of leakage current. IEEE Transactions on Dielectrics and Electrical Insulation, 24(4), 2274–2281.CrossRefGoogle Scholar
  21. Miyazaki, T., & Okabe, S. (2010). Experimental investigation to calculate the lightning outage rate of a distribution system. IEEE Transactions on Power Delivery, 25(4), 2913–2922.CrossRefGoogle Scholar
  22. Munukutla, K., Vittal, V., Heydt, G. T., Chipman, D., & Keel, B. (2010). Practical evaluation of surge arrester placement for transmission line lightning protection. IEEE Transaction on Power Delivery, 25(3), 1742–1748.CrossRefGoogle Scholar
  23. Piantini, A. (2008). Lightning protection of overhead power distribution lines. In 29th International Conference on Lightning ProtectionICLP, Uppsala, Sweden (pp. 1–29).Google Scholar
  24. Saldivar-Guerrero, R., Hernández-Corona, R., Lopez-Gonzalez, F. A., Rejón-García, L., & Romero-Baizabal, V. (2014). Application of unusual techniques for characterizing ageing on polymeric electrical insulation. Electric Power Systems Research, 117, 202–209.CrossRefGoogle Scholar
  25. Salles, C., Picanco, A. F., Martinez, M. L. B., & Oliveira, H. R. P. M. (2009). Determination of the discharge current on distribution network surge arresters. In IEEE PowerTech.Google Scholar
  26. Seyyedbarzegar, S. M., & Mirzaie, M. (2016). Thermal balance diagram modelling of surge arrester for thermal stability analysis considering ZnO varistor degradation effect. IET Generation, Transmission and Distribution, 10(7), 1570–1581.CrossRefGoogle Scholar
  27. Silva, I. N., Spatti, D. H., Flauzino, R. A., Liboni, L. H. B., & Reis, S. F. (2017). Artificial neural networks: A practical course (1st ed.). Switzerland: Springer.CrossRefGoogle Scholar
  28. Spatti, D. H., & Liboni, L. H. B. (2016). Computational tools for data processing in smart cities. Smart cities technologies. In InTech (1st ed., pp. 41–54).Google Scholar
  29. Wang, Y., Xie, P., Zhao, J. C., & Zhang, G. F. (2008). Simulation of line surge arresters for lightning protection in 10 kV transmission lines. In IEEE CICEDChina International Conference on Electricity Distribution.Google Scholar
  30. Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed.). Burlington: Morgan Kaufmann.zbMATHGoogle Scholar
  31. Zhao, X., Xu, C., Ren, L., Liao, R., Yang, L., Li, J., et al. (2018). Effect of impulse current degradation on the electrical properties and dielectric relaxations of ZnO-based ceramic varistors. IEEE Transactions on Dielectrics and Electrical Insulation, 25(3), 975–983.CrossRefGoogle Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  • Marcel A. Araújo
    • 1
  • Danilo H. Spatti
    • 2
  • Luisa H. B. Liboni
    • 3
  • Luiz A. Pergentino
    • 4
  • Fabricio E. Viana
    • 4
  • Rogério A. Flauzino
    • 5
  1. 1.Academic Units Cabo de Santo Agostinho (UACSA)Federal Rural University of Pernambuco (UFRPE)Cabo de Santo AgostinhoBrazil
  2. 2.Institute of Mathematics and Computer Sciences (ICMC)University of São Paulo (USP)São CarlosBrazil
  3. 3.Department of Electrical and Computer Engineering (CEC)Federal Institute of Education, Science, and Technology of São Paulo (IFSP)SertãozinhoBrazil
  4. 4.EDP BandeiranteMogi das CruzesBrazil
  5. 5.Department of Electrical and Computer Engineering, São Carlos School of EngineeringUniversity of São Paulo (USP)São CarlosBrazil

Personalised recommendations