Advertisement

Arabian Journal for Science and Engineering

, Volume 44, Issue 3, pp 1809–1822 | Cite as

A Novel Three-Phase Inverter Fault Diagnosis System Using Three-dimensional Feature Extraction and Neural Network

  • Muhammad TalhaEmail author
  • Furqan Asghar
  • Sung Ho Kim
Research Article - Electrical Engineering
  • 59 Downloads

Abstract

In general, fault diagnosis and classification is concerned about monitoring a system, identifying the fault occurrence and pinpointing the exact fault location. Fault diagnosis and fault-tolerant systems are crucial for modern electrical and electronic system safety. Inverters are most frequently used DC–AC power converters. DC–AC three-phase inverter faults are very common to occur and need an effective and robust fault diagnosis system for protection and isolation. A new fault detection and classification technique is introduced in this paper. Feature extraction system considers three-phase voltage pattern plot in 3D, and the neural network are used to pinpoint the fault location. The testing process is conducted under simulation and physical environment to confirm its expediency. Proposed technique and experimental results are discussed in detail. Simulation experiment followed by practical implementation on SPWM three-phase inverter is included with results to ensure the system accuracy and reliability.

Keywords

Three-phase inverter DsPIC30F4011 Fault diagnosis and classification Feature extraction Neural network LabVIEW 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kim, D.-E.; Lee, D.-C.: Fault diagnosis of three phase PWM inverters using wavelet and SVM. J. Power Electron. 9(3), 377–385 (2016)Google Scholar
  2. 2.
    Kadri, F.; Drid, S.; Djeffal, F.: Neural classification method in fault detection and diagnosis for voltage source inverter in variable speed drive with induction motor. In: Eighth international conference and exhibition on ecological vehicles and renewable energies, IEEE (2013).Google Scholar
  3. 3.
    Ubale, M.R.; Dhmale, R.B.; Lokhande, S.D.: Open switch fault diagnosis in three phase inverter using diagnostic variable method. Int. J. Res. Eng. Technol. 2(12), 636–641 (2013)Google Scholar
  4. 4.
    Ibrahim, S.O.; Faris, K.N.; Elzahab, EAbo: Implementation of fuzzy modeling system for faults detection and diagnosis in three phase induction motor drive system. J. Electr. Syst. Inf. Technol. 2(2), 27–46 (2015)Google Scholar
  5. 5.
    Phogat, S.: Analysis of single-phase SPWM inverter. Int. J. Sci. Res. 3, 1793–1798 (2014)Google Scholar
  6. 6.
    Martins, J.F.; Pires, V.F.; Lima, C.; Pires, A.J: Fault detection and diagnosis of grid-connected power inverters using PCA and voltage mean value, IEEE (2012).Google Scholar
  7. 7.
    Peuget, R.; Courtine, S.; Rognon, J.-P.: Fault detection and isolation on a PWM inverter by knowledge-based model. IEEE Trans. Ind. Appl. 34(6), 1318–1326 (1998)Google Scholar
  8. 8.
    Khomfoi, S.; Tolbert, L.M.: Fault diagnostic system for a multilevel inverter using a neural network. IEEE Trans. Power Electron. 22(3), 1062–1069 (2007)Google Scholar
  9. 9.
    Zidani, F.; Diallo, D.; Benbouzid, El Hachemi; Nait- Said, R.: A fuzzy-based approach for the diagnosis of fault modes in a voltage-fed PWM inverter induction motor drive. IEEE Trans. Ind. Electron. 55(2), 586–593 (2008)Google Scholar
  10. 10.
    Ko, Y.-J.; Lee, K.-B.: Fault diagnosis of a voltage-fed PWM inverter for a three-parallel power conversion system in a wind turbine. J. Power Electron. 10(6), 686–693 (2010)Google Scholar
  11. 11.
    Asghar, F.; Talha, M.; Kim, S.H.: Neural network based fault detection and diagnosis system for three-phase inverter in variable speed drive with induction motor. J. Control Sci. Eng. 2016, 1286318 (2016).  https://doi.org/10.1155/2016/1286318 zbMATHGoogle Scholar
  12. 12.
    Kadri, F.; Drid, S.; Djeffal, F.; Chrifi-Alaoui, L.: Neural classification method in fault detection and diagnosis for voltage source inverter in variable speed drive with induction motor, In: Proceedings of the 8th international conference and exhibition on ecological vehicles and renewable energies (EVER ’13), pp. 1–5, Monte Carlo, Monaco (2013)Google Scholar
  13. 13.
    Geometry, V.; Selvaperumal, S.: Fault detection and classification with optimization techniques for three phase single inverter circuit. J. Power Electron. 16, 1–14 (2016)Google Scholar
  14. 14.
    Ubale, M.; Dhumale, R.B.; Dixit, V.V.; Lokhande, S.D.: Method of open switch fault detection in three phase inverter using artificial neural network. Int. J. Res. Sci. Adv. Technol. 3(3), 78–82 (2013)Google Scholar
  15. 15.
    Gomathy, V.; Selvaperumal, S.: Fault detection and classification with optimization techniques for three phase single inverter circuit. J. Power Electron. 16(3), 1–14 (2016)Google Scholar
  16. 16.
    Im, W.-S.; Kim, J.-S.; Lee, D.-C.; Lee, K.-B.: Diagnosis methods for IGBT open switch fault applied to three phase AC/DC PWM converter. J. Power Electron. 12(1), 120–127 (2012)Google Scholar
  17. 17.
    Tang, K.W.; Skorin-Kapov, J.: Training artificial neural networks: backpropagation via nonlinear optimization. J. Comput. Inf. Technol. 9(1), 1–14 (2001)zbMATHGoogle Scholar
  18. 18.
    Ubale, M.R.; Dhumale, R.B.; Lokhande, S.D.: Open switch fault diagnosis in three phase inverter using diagnostic variable method. Int. J. Res. Eng. Technol. 2(12), 636–641 (2013)Google Scholar
  19. 19.
    Khanniche, M.S.; Mamat-Ibrahim, M.R.: Fault detection and diagnosis of three phase inverter system. Power Eng. 69–75 (2001)Google Scholar
  20. 20.
    Dhumale, R.B.; Lokhande, S.D.; Thombare, N.D.: Ghatule. M.P.: Fault detection and diagnosis of high speed switching device in power inverters. Int. J. Res Eng. Technol. 4(2), 253–257 (2015)Google Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringKunsan National UniversityKunsanKorea
  2. 2.School of Electrical EngineeringThe University of FaisalabadFaisalabadPakistan

Personalised recommendations