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Optimal estimation of power system harmonics using a hybrid Firefly algorithm-based least square method

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Abstract

This paper presents a new hybridized algorithm known as Firefly algorithm-based least square (FA-LS) method for power system harmonic estimation. It uses a Firefly algorithm-based approach to estimate the phases and a least-square method for estimating the amplitudes of the harmonic signals. The simulation results are presented to demonstrate the estimation accuracy of FA-LS with the recently proposed particle swarm optimization with passive congregation and artificial bee colony with LS algorithms. The obtained results of FA-LS reveal that this algorithm is best in terms of accuracy and computational time. Practical validation is also made with the experimentation of the algorithm with real-time data obtained from a switch mode power supply with the power quality analyzer and estimation is performed under simulating environment.

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References

  • Bettayeb M, Qidwai U (1998) Recursive estimation of power system harmonics. Electric Power Syst Res 47:143–152

    Article  Google Scholar 

  • Bettayeb M, Qidwai U (2003) A hybrid least squares-ga-based algorithm for harmonic estimation. IEEE Trans Power Deliv 18(2):377–382

    Article  Google Scholar 

  • 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 

  • Chen CI, Chen YC (2014) Comparative study of harmonic and inter harmonic estimation methods for stationary and time-varying signals. IEEE Trans Ind Electron 61(1):397–404

    Article  Google Scholar 

  • Erin L, Devaney MJ (2002) Calculation of power system harmonics via wavelet packet decomposition in real time metering. In: Proceedings of the 19th IEEE instrumentation measurement technology conference, vol 2, pp 1643–1647

  • George TA, Bones D (1991) Harmonic power flow determination using the Fast Fourier Transform. IEEE Trans Power Deliv 6:530–535

    Article  Google Scholar 

  • He S, Wu QH, Wen JY, Saunders JR, Paton RC (2004) A particle swarm optimizer with passive congregation. Bio Syst 78(1–3):135–147

    Google Scholar 

  • Jain Sachin K, Singh SN (2011) Harmonics estimation in emerging power system: key issues and challenges. Electr Power Syst Res 81:1754–1766

    Article  Google Scholar 

  • Lin HC (2012) Power harmonics and inter harmonics measurement using recursive group-harmonic power minimizing algorithm. IEEE Trans Ind Electron 59(2):1184–1193

    Article  Google Scholar 

  • Lu Z, Ji TY, Tang WH, Wu QH (2008) Optimal harmonic estimation using a particle swarm optimizer. IEEE Trans Power Deliv, pp 1166–1174

  • Mishra S (2002) Optimal design of power system stabilizers using particle swarm optimization. IEEE Trans Energy Convert 17(3):406–413

    Article  Google Scholar 

  • Mishra S (2005) A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation. IEEE Trans Evolut Comput 9:61–73

    Article  Google Scholar 

  • Saiz VMM, Guadalupe JB (1998) Application of Kalman filtering for continuous real-time tracking of power system harmonics. Proc Gener Transm Distrib 144(1):13–20

    Article  Google Scholar 

  • Santoso S, Powers EJ, Grady WM, Hofmann P (1996) Power quality assessment via wavelet transform analysis. IEEE Trans Power Deliv 11(2):924–930

    Article  Google Scholar 

  • Testing and Measurement Techniques (2009) General guide on harmonics and inter harmonics measurements and instrumentation, for power supply systems and equipment connected thereto. IEC Std. 61000-4-7

  • Yang XS (2005) Biology-derived algorithms in engineering optimization (Chapter 32). In: Olarius, Zomaya (eds.) Handbook of bioinspired algorithms and applications. Chapman and Hall/CRC, Boca Raton

  • Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, UK

    Google Scholar 

  • Yang XS (2010) Engineering optimization: an introduction to metaheuristic applications. Wiley, New York

    Book  Google Scholar 

Download references

Acknowledgments

This work was supported by SERB Project No. SR/FTP/ETA-12/2011, Department of Science and Technology (DST), Govt. of India.

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Correspondence to Santosh Kumar Singh.

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Dr. Arup Kumar Goswami has received the research grant from Department of Science and Technology (DST), Govt. of India. Santosh Kumar Singh and Nilotpal Sinha are the research scholars under Dr. Arup Kumar Goswami and Prof. Nidul Sinha. The authors would like to acknowledge the Department of Science and Technology (DST), Govt. of India, for the financial help provided for completing this work. The authors also acknowledge electrical engineering department of NIT Silchar for providing such infrastructure for completing the work.

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Communicated by V. Loia.

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Singh, S.K., Sinha, N., Goswami, A.K. et al. Optimal estimation of power system harmonics using a hybrid Firefly algorithm-based least square method. Soft Comput 21, 1721–1734 (2017). https://doi.org/10.1007/s00500-015-1877-0

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