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A neural network based approach to estimate of power system harmonics for an induction furnace under the different load conditions

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Abstract

This study presents an artificial neural network based intelligent monitoring algorithm to detect of a power system harmonics. The proposed approach was tested on the current and voltage data of an induction furnace power system, which was collected by using a LabVIEW based measurement system under different load conditions. The collected data was analyzed with MATLAB program using the short-time Fourier transform and power spectral density estimation methods to find the dominant harmonics of the power system. After determination of the operational region in terms of the harmonic levels under the full-load condition, the power spectral density algorithm was used for determining the stationary intervals of the power system data. Identified harmonic frequencies were used to train the artificial neural network algorithm, which was then tested for harmonic estimation at different load conditions. Hence, the neural network topology was used as a artificial follower. Results demonstrate that harmonic state estimation of a power system can be achieved by an error variation at the output of the auto-associative neural network.

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References

  1. Gokozan H (2011) Power quality monitoring and analysis of industrial factories and optimum harmonic filter selection. Dissertation, Sakarya University Pure and Applied Sciences

  2. Kadir MZ, Othman SST, Lombigit L (2008) On-line power quality monitoring system using compactRIO device: a review on system development. Eur J Sci Res 21(2):288–295

    Google Scholar 

  3. Lin HC (2007) Intelligent neural network-based fast power system harmonic detection. IEEE Trans Ind Electron 34(1):43–52

    Article  Google Scholar 

  4. Valtierra-Rodriguez M, Osornio-Rios RA, Garcia-Perez A et al (2013) FPGA-based neural network harmonic estimation for continuous monitoring of the power line in industrial applications. Electr Power Syst Res 98:51–57

    Article  Google Scholar 

  5. Zhang G, Xu W (2008) Estimating harmonic distortion levels for systems with random-varying distributed harmonic-producing loads. IET Gener Transm Distrib 2(6):847–855

    Article  Google Scholar 

  6. Biswas S, Chatterjee A, Goswami SK (2013) An artificial bee colony-least square algorithm for solving harmonic estimation problems. Appl Soft Comput 13:2343–2355

    Article  Google Scholar 

  7. Jain SK, Singh SN (2013) Fast harmonic estimation of stationary and time-varying signals using EA-AWNN. IEEE Trans Instrum Meas 62(2):335–343

    Article  MathSciNet  Google Scholar 

  8. Temurtas H, Temurtas F (2011) An application of neural networks for harmonic coefficients and relative phase shifts detection. Expert Syst Appl 38(4):3446–3450

    Article  Google Scholar 

  9. Vasumathi B, Moorthi S (2012) Implementation of hybrid ANN-PSO algorithm on FPGA for harmonic estimation. Eng Appl Artif Intell 25:476–483

    Article  Google Scholar 

  10. Nascimentoa CF, Oliveira AA Jr, Goedtelc A, Dietricha AB (2013) Harmonic distortion monitoring for nonlinear loads using neural-network-method. Appl Soft Comput 13:475–482

    Article  Google Scholar 

  11. Taskin S, Gokozan H (2010) Determination of the spectral properties and harmonic levels for driving an induction motor by an inverter driver under the different load conditions. Electron Electr Eng 2(108):79–84

    Google Scholar 

  12. Bayram D, Seker S (2013) Wavelet based neuro detector for low frequencies of vibration signals in electric motors. Appl Soft Comput 13:2683–2691

    Article  Google Scholar 

  13. Swiatek R, Rogoz M, Hanzelka Z (2007) Power system harmonic estimation using neural networks. In: 9th international conference electrical power quality and utilisations, Barcelona, 9–11 October

  14. Gabor D (1946) Theory of communication. IEEE 93(3):429–457

    Google Scholar 

  15. Seker S, Ayaz E (2002) A study of condition monitoring for induction motors under accelerated aging process. IEEE Power Eng Rev 22(7):35–36

    Article  Google Scholar 

  16. Taskin S, Seker S, Karahan M, Akinci C (2009) Spectral analysis for current and temperature measurements in power cables. Electr Power Compon Syst 37:415–426

    Article  Google Scholar 

  17. Akinci C, Seker S, Taskin S (2012) Spectral and statistical analysis for ferroresonance phenomenon in electric power systems. Electr Eng 94:117–124

    Article  Google Scholar 

  18. Vaseghi SV (2008) Advanced digital signal processing and noise reduction, 4th edn. Wiley, United Kingdom

    Book  Google Scholar 

  19. Othman MR, Zhang Z, Imamura T, Miyake T (2009) Modeling driver operation behavior by linear prediction analysis and auto associative neural network. In: Proceedings IEEE international conference on systems, man and cybernetics (SMC2009), pp 649–653

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Acknowledgments

The authors present their special thanks to the Celal Bayar University Scientific Research Projects Commission for supporting this study under Project Number 2012-052, and technical/engineering units of the Turcelik Metal Casting Plant in Turgutlu-Manisa / Turkey for their technical supports in this research.

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Authors and Affiliations

  1. Department of Electric and Energy Technologies, Celal Bayar University, Turgutlu, Manisa, Turkey

    Hayrettin Gokozan

  2. Engineering Faculty, Department of Electrical and Electronics Engineering, Celal Bayar University, 45140, Manisa, Turkey

    Sezai Taskin

  3. Department of Electrical Engineering, Istanbul Technical University, Istanbul, Turkey

    Serhat Seker

  4. Faculty of Engineering and Natural Sciences, Suleyman Sah University, Istanbul, Turkey

    Huseyin Ekiz

Authors
  1. Hayrettin Gokozan
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  2. Sezai Taskin
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  3. Serhat Seker
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  4. Huseyin Ekiz
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Correspondence to Sezai Taskin.

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Gokozan, H., Taskin, S., Seker, S. et al. A neural network based approach to estimate of power system harmonics for an induction furnace under the different load conditions. Electr Eng 97, 111–117 (2015). https://doi.org/10.1007/s00202-014-0320-3

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  • DOI: https://doi.org/10.1007/s00202-014-0320-3

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