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Paraconsistent Artificial Neural Network for Structuring Statistical Process Control in Electrical Engineering

  • João Inácio da Silva FilhoEmail author
  • Clovis Misseno da Cruz
  • Alexandre Rocco
  • Dorotéa Vilanova Garcia
  • Luís Fernando P. Ferrara
  • Alexandre Shozo Onuki
  • Mauricio Conceição Mario
  • Jair Minoro Abe
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 110)

Abstract

In this study, we present an algorithmic structure based on paraconsistent annotated logic (PAL) that can simulate the calculi of average values present in a dataset and detect the variations of the average using only PAL concepts. We call the structure as paraconsistent artificial neural network for extraction of moving average (PANnet\(_\mathrm{{MovAVG}})\). As an example of its application, we use PANnet\(_\mathrm{{MovAVG}}\) to assist in the analysis of a final product quality index related to electrical engineering. To obtain the final result, we applied PANnet\(_\mathrm{{MovAVG}}\) to simulate the statistical behavior of the Statistical Process Control (SPC) by comparing values obtained with a ranking that establishes quality index standards based on electrical power distribution. First, tests were conducted using data with random values to verify the behavior of PANnet\(_\mathrm{{MovAVG}}\) and to set the optimum number of algorithms to form an optimized computational structure. Then, we used a database with actual electric voltage values generated by an electrical power system of an electrical power utility grid in Brazil. In the various tests, PANnet\(_\mathrm{{MovAVG}}\) appropriately detected changes and identified variations of electric voltage in 220-V transmission lines. The results show that PANnet\(_\mathrm{{MovAVG}}\) can be used to construct an efficient architecture for determining and monitoring quality scores with applications in various areas of engineering, especially for detecting quality index in an electricity distribution network.

Keywords

Paraconsistent annotated logic Statistical process control Electrical power system Energy quality 

References

  1. 1.
    Abe, J.M., Akama, S., Nakamatsu, K.: Introduction to Annotated Logics—Foundations for Paracomplete and Paraconsistent Reasoning, Series Title Intelligent Systems Reference Library, Vol. 88. Publisher Springer International Publishing, Copyright Holder Springer International Publishing Switzerland, eBook ISBN 978-3-319-17912-4, doi: 10.1007/978-3-319-17912-4, Hardcover ISBN 978-3-319-17911-7, Series ISSN 1868-4394, Edition Number 1, 190 pages (2015)
  2. 2.
    Abe, J.M.: Paraconsistent intelligent based-systems: new trends in the applications of paraconsistency, editor. Book Series: Intelligent Systems Reference Library, Vol. 94, 306 pages. Springer-Verlag, Germany (2015). ISBN 978-3-319-19721-0Google Scholar
  3. 3.
    BRAZIL-National Electric Energy Agency (Agência Nacional de Energia Elétrica)—ANEEL. In Portuguese: Atlas of Brazil’s Electrical Energy. (Atlas de Energia Elétrica do Brasil). (2008). http://www.aneel.gov.br/arquivos/PDF/atlas_par1_cap2.pdf. Accessed 30 July 2015
  4. 4.
    BRAZIL-National Electric Energy Agency (Agência Nacional de Energia Elétrica)—ANEEL. In Portuguese: Procedures for the distribution of electricity in the National Electrical System (Procedimentos de Distribuição de Energia Elétrica no Sistema Elétrico Nacional—PRODIST) (2009). http://www.aneel.gov.br/arquivos/pdf/modulo8_24032006_srd.pdf. Accessed 20 May 2015
  5. 5.
    Corduas, M.: Bootstrapping moving average models. J. Italian Stat. Soc. 1, (2), 227–234 (1992). doi: 10.1007/BF02589032 (Springer-Verlag)
  6. 6.
    Da Costa, N.C.A., Abe, J.M., Subrahmanian, V.S.: Remarks on annotated logic. Zeitschrift f. math. Logik und Grundlagen d. Math. 37, 561–570 (1991)Google Scholar
  7. 7.
    Da Silva Filho, J.I., Lambert-Torres, G., Abe, J.M.: Uncertainty Treatment Using Paraconsistent Logic: Introducing Paraconsistent Artificial Neural Networks, pp. 211, 328. Frontiers in Artificial Intelligence and Applications. IOS Press, Amsterdam, Netherlands (2010)Google Scholar
  8. 8.
    Da Silva Filho, J.I. et al.: Paraconsistent Logic Algorithms Applied to Seasonal Comparative Analysis with Biomass Data Extracted by the Fouling Process. Paraconsistent Intelligent- Based Systems: New Trends in the Applications of Paraconsistency: Intelligent Systems Reference Library, pp 131–152. Springer International Publishing AG, Switzerland (2015). doi: 10.1007/978-3-319-19722-7
  9. 9.
    Da Silva Filho, J.I., Rocco, A., Mario, M.C., Ferrara, L.F.P.: Annotated Paraconsistent logic applied to an expert System Dedicated for supporting in an Electric Power Transmission Systems Re- Establishment. IEEE Power Engineering Society—PSC 2006, pp. 2212–2220. Atlanta, USA (2006). ISBN-1- 4244-0178-XGoogle Scholar
  10. 10.
    Dugan, R.C, McGranaghan, M.F., Santos, S., Beaty, H.W.: Electrical Power System Quality, 2nd edn., p. 528. McGraw Hill (2003)Google Scholar
  11. 11.
    Jacob, A.L., Pillai, S.K.: Statistical process control to improve coding and code review. IEEE Softw. 20(3), 50–55 (2003)CrossRefGoogle Scholar
  12. 12.
    Jelali, M.: Statistical Process Control. Control Performance Management in Industrial Automation Part of the series Advances in Industrial Control, pp. 209–217. Springer, London (2013). doi: 10.1007/978-1-4471-4546-2_8
  13. 13.
    Montgomery, D.C.: Introduction to Statistical Quality Control, 4th edn. Wiley (2001). Young, M.: The Technical Writer’s Handbook. University Science, Mill Valley, CA (1989)Google Scholar
  14. 14.
    Misseno da Cruz, C. et al.: Application of Paraconsistent Artificial Neural network in Statistical Process Control acting on voltage level monitoring in Electrical Power Systems. Intelligent System Application to Power Systems (ISAP), pp. 1–6. Porto–PT (2015). doi: 10.1109/ISAP.2015.7325579
  15. 15.
    Naikan, V.N.A.: Statistical Process. Control Handbook of Performability Engineering, pp. 187–201. Springer, London (2008). doi: 10.1007/978-1-84800-131-2
  16. 16.
    Sancho, J., Pastor, J.J., Martínez, J., García M.A.: Evaluation of Harmonic Variability in Electrical Power Systems through Statistical Control of Quality and Functional Data Analysis. The Manufacturing Engineering Society International Conference, MESIC (2013)Google Scholar
  17. 17.
    Shewhart, W.A.: Economic Control of Quality of Manufactured Product, p. 501. Van Nostrand, New York (1931)Google Scholar
  18. 18.
    Subrahmanian, V.S.: On the semantics of quantitative logic programs. In: Proceedingsof 4th IEEE Symposium on Logic Programming. Computer Society Press, Washington DC (1987)Google Scholar
  19. 19.
    WESTERN ELECTRIC: Statistical Quality Control Handbook, 2nd edn. Western Electric Company, Indianapolis (1958)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • João Inácio da Silva Filho
    • 1
    Email author
  • Clovis Misseno da Cruz
    • 1
  • Alexandre Rocco
    • 1
  • Dorotéa Vilanova Garcia
    • 1
  • Luís Fernando P. Ferrara
    • 1
  • Alexandre Shozo Onuki
    • 1
  • Mauricio Conceição Mario
    • 1
  • Jair Minoro Abe
    • 1
    • 2
  1. 1.Research Group in Paraconsistent Logic Applications, UNISANTASanta Cecília UniversitySantos CityBrazil
  2. 2.Graduate Program in Production EngineeringICET, Paulista UniversitySão PauloBrazil

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