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Evaluation of the Susceptibility of Failures in Steel Structures of Transmission Lines

  • Ricardo Nunes Wazen
  • Thelma S. P. FernandesEmail author
  • Alexandre R. Aoki
  • Wyrllen E. de Souza
Article

Abstract

Overhead transmission lines are exposed to several risks associated with its surroundings, changes in design characteristics and climatic variations. Many times, these risks can lead to serious damages, incurring in drop towers. So, this work has the objective of evaluation of the susceptibility of the structures to suffer mechanical efforts above the projected, which can lead to their falls. It is made taking the database of the structures and information about the ones that had suffered damages and using the Rough Sets Theory and the Regression Logistic Method to extract knowledge about what parameters and variables influence the mechanic behavior of the operation lines and can be applied to diagnose possible dropped towers. The results are obtained using the history of two thousand steel structures currently operating in the state of Parana, from which rules are generated to identify the susceptibility of fall.

Keywords

Dropped structures Logistic regression Metallic structures Rough sets Transmission lines 

Notes

Acknowledgments

The authors gratefully acknowledge COPEL for enabling the analysis and provide data for development and application of this work.

References

  1. Araújo, A. C. M. (2007). Perdas e Inadimplência na Atividade de Distribuição de Energia Elétrica no Brasil. Tese de Doutorado da Universidade Federal do Rio de Janeiro, Rio de Janeiro.Google Scholar
  2. Atlas Geográfico do Brasil. Editora Melhoramentos. (2002). Accessed 9 November, 2011, from http://www.uol.com.br/atlas.
  3. Bonaldi, E. L., Lambert-Torres, G., Silva, L. E. B., & Oliveira, L. E. L. (2002). A rough sets based classifier for induction motors fault diagnosis. WSEAS Transactions on Systems, Londres, 2(2), 230–237.Google Scholar
  4. Cao, Y. J., Feng, L., & Qiu, J. J. (2004). Performance of the novel rough fuzzy-neural network on short-term load forecasting. IEEE Power Systems Conference and Exposition, 1, 543–547.Google Scholar
  5. Chen, T. C., & Pai, P. F. (2009). Rough set theory with discriminant analysis in analyzing electricity loads. ELSEVIER Expert Systems with Aplications, 3, 8799–8806. Google Scholar
  6. Chien, C. F., Peng, J. T., & Tseng, T. L. B. (2004). Rough set theory for data mining for fault diagnosis on distribution feeder. IEEE Proceedings Generation, Transmission and Distribution, 151, 689–697.Google Scholar
  7. COMPANHIA PARANAENSE DE ENERGIA. (2007). Atlas do Potencial Eólico do Estado do Paraná. Curitiba.Google Scholar
  8. Coutinho, M. P. (2007). Detecção de Ataques em Infra- Estruturas Críticas de Sistemas Elétricos de Potência usando Técnicas Inteligentes. Tese de Doutorado da Universidade Federal de Itajubá.Google Scholar
  9. Crossley, P. A., Hor, C. L., & Watson, S. J. (2007). Building knowledge for substation-based decision support using rough sets. IEEE Transactions on Power Delivery, 22(3), 1372–1379.Google Scholar
  10. Fuchs, R. D. (1977). Transmissão de Energia Elétrica: Linhas Aéreas (Vol. 1). Rio de Janeiro: Livros Técnicos e Científicos Editora.Google Scholar
  11. Johnson, R. A., & Wycherd, D. W. (1998). Applied multivariate statistical analysis (4th ed.). Upper Saddle River, NJ: Prentice Hall Inc.Google Scholar
  12. Kleinbaum, D. G. (1994). Logistic regression: A self learning text. New York: Springer.zbMATHGoogle Scholar
  13. Labegalini, P. R., Labegalini, J. A., Fuchs, S. R. D., & Almeida, M. T. (1992). Projetos Mecânicos das Linhas Aéreas de Transmissão (2nd ed.). São Paulo: Edgard Blücher.Google Scholar
  14. Lachenbruch, P. A. (1975). Discriminant analysis. New York: Halfner.zbMATHGoogle Scholar
  15. Lambert-Torres, G. (2002). Application of rough sets in power system control center data mining. In IEEE Power Engineering Society winter meeting (Vol. 1, pp. 627–631).Google Scholar
  16. Martins, R. (2010). Classificação de Transformadores de Distribuição de Energia Elétrica quanto à DHTV usando Rough Sets. Dissertação de Mestrado da Universidade Federal do Paraná, Curitiba.Google Scholar
  17. Neter, J., Kutner, M. H., & Wasserman, W. (1996). Applied linear regression models (3rd ed.). New York: McGraw-Hill.Google Scholar
  18. Ohrn, A. (1999). Discernibility and rough sets in medicine: Tools and applications. Norway: Norwegian University of Science and Technology.Google Scholar
  19. Ohrn, A. (2001). Rosetta: Technical reference manual. Norway: Knowledge Systems Group, Department of Computer and Information Science, Norwegian University of Science and Technology.Google Scholar
  20. Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11, 341–356.Google Scholar
  21. Penin, C. A. de S. (2008). Combate. Prevenção e Otimização de Perdas Comerciais de Energia Elétrica. Dissertação de Mestrado da Universidade de São Paulo, São Paulo.Google Scholar
  22. Rodrigues, R. (2010). Metodologia de Análise Elétrica de Impedimentos Programados de Sistemas Elétricos utilizando Fluxo de Potência e Rough Sets. Dissertação de Mestrado da Universidade Federal do Paraná, Curitiba.Google Scholar
  23. Souza, W. E. (2008). Análise das Distorções Harmônicas de Tensão a partir de Características dos Transformadores e de Dados de Consumo. Dissertação de Mestrado da Universidade Federal do Paraná, Curitiba.Google Scholar
  24. Wazen, R. N. (2008). Proposta de Aplicação da Manutenção Baseada em Confiabilidade para Linhas de Transmissão da COPEL. Monografia de Especialização da Universidade Tecnológica Federal do Paraná.Google Scholar
  25. Wazen, R. N. (2011). Avaliação da Suscetibilidade de Falhas em Estruturas Metálicas de Linhas de Transmissão. Dissertação de Mestrado da Universidade Federal do Paraná.Google Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2013

Authors and Affiliations

  • Ricardo Nunes Wazen
    • 1
  • Thelma S. P. Fernandes
    • 1
    Email author
  • Alexandre R. Aoki
    • 2
  • Wyrllen E. de Souza
    • 3
  1. 1.Department of Electrical EngineeringFederal University of Paraná (UFPR), Centro PolitécnicoCuritibaBrazil
  2. 2.Department of Electrical EngineeringFederal University of Paraná (UFPR) and Eletroelectronic Department, Institute of Technology for Development (LACTEC), Centro PolitécnicoCuritibaBrazil
  3. 3.Department of Electrical EngineeringEletroelectronic Department, Institute of Technology for Development (LACTEC), Centro PolitécnicoCuritibaBrazil

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