Analysis of Power System Harmonics Using PSNR Metric

  • Srihari MandavaEmail author
  • Ramesh Varadarajan
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 436)


In recent times there has been a wide interest in micro grids. One area of concern in micro grids is the generation of harmonics by active devices such as converters and FACTS devices used for reactive power compensation. The currently available literature focuses on the number of operations and fundamental cycles for estimating harmonics. This usually results in a trade-off between accuracy of estimation and the choice of digital filter parameters. In this work a novel orthogonal frequency division multiplexing (OFDM) principle modified as per the power system scenario has been proposed. Odd harmonics up to 31st order are measured by demodulation as if the power signal is OFDM modulated. All these harmonics are measured using only one cycle of voltage signal. Instantaneous detection of harmonics is made possible using the Discrete Wavelet Transform (DWT) instead of the fast Fourier transforms used in conventional OFDM. DWT is also used for noise elimination before the harmonics are analyzed and the performance of proposed method is analyzed using PSNR under different noise conditions.


Harmonics OFDM Demodulation FFT DWT PSNR 


  1. 1.
    Tolbert, L.M., Hollis, H.D., Hale, Jr., P.S.: Survey of harmonics measurements in electrical distribution systems. IEEE IAS Annual Meeting, San Diego, CA, pp. 2333–2339 (1996)Google Scholar
  2. 2.
    Ji, T.Y., Li, M.S., Wu, Q.H., Jiang, L.: Optimal estimation of harmonics in a dynamic environment using an adaptive bacterial Swarming algorithm. IET Gener. Trans. Distrib. 5(6), 609–620 (2011)CrossRefGoogle Scholar
  3. 3.
    Rakpenthai, C., Uatrongjit, S., Watson, N.R., Suttichai, P.: On harmonic state estimation of power system with uncertain network parameters. In: Transmission & Distribution Conference and Exposition, IEEE Power and Energy Society, Chicago, IL, pp. 0885–8950 (2014)Google Scholar
  4. 4.
    Sadinezhad, I., Agelidis, V.G.: Real-time power system phasors and harmonics estimation using a new decoupled recursive-least-squares technique for DSP implementation. IEEE Trans. Ind. Electron. 60(6), 2295–2308 (2013)CrossRefGoogle Scholar
  5. 5.
    Marques, C.A.G., Ribeiro, M.V., Duque, C.A., Ribeiro, P., Da Silva, E.A.B.: A controlled filtering method for estimating harmonics of off-nominal frequencies. IEEE Trans. Smart Grid. 3(1), 38–49 (2012)CrossRefGoogle Scholar
  6. 6.
    Maofa, G., Xiaocong, L., Longqing, C., Liming, G., Guoliang, L.: Harmonic analysis approach based on wavelet transform and neural network. In: 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Weihai, China, pp. 774–576 (2011)Google Scholar
  7. 7.
    Jain, S.K., Singh, S.N.: Low-order dominant harmonics estimation using adaptive wavelet neural network. IEEE Trans. Ind. Electron. 61(1), 428–435 (2013)CrossRefGoogle Scholar
  8. 8.
    Duque, C.A., Silveira, P.M., Ribeiro, P.F.: Visualizing time varying harmonics using filter bank. Electr. Power Syst. Res. 81(4), 974–983 (2011)CrossRefGoogle Scholar
  9. 9.
    Djuric, P.M., Begovic, M.M., Doroslovaeki, M.: Instantaneous phase tracking in power networks by demodulation. IEEE Trans. Instrum. Meas. 41(6), 314–319 (1992)CrossRefGoogle Scholar
  10. 10.
    Kamwa, I., Leclerc, M., McNabb, D.: Performance of demodulation-based frequency measurement algorithms used in typical PMUs. IEEE Trans. Power Deliv. 19(2), 505–514 (2004)CrossRefGoogle Scholar
  11. 11.
    Kashyap, S., Singh, A.K.: Most suitable mother wavelet for measurement of power system harmonics using DWT in view of IEEE standard. In: 13th International Conference on Harmonics and Quality of Power, Wollongong, Australia, pp. 1459–2000 (2008)Google Scholar
  12. 12.
    Jaipradidtham, C., Kinnares, V.: Harmonic flow analysis of HV DC systems for discrete wavelet transform with advanced of the harmonic impedances and voltage source transients model of AC-DC converters. In: TENCON, IEEE Region 10 Conference, Hong Kong, China, pp. 1–4 (2006)Google Scholar
  13. 13.
    Bodhe, R., Joshi, S., Narkhede, S.: Comparative analysis of the BER performance of DWT OFDM over that of FFT OFDM in presence of phase noise. In: International Conference on Robotics, Automation, Control and Embedded Systems, Chennai, India, pp. 18–20 (2015)Google Scholar
  14. 14.
    Srinivasan, K., Dauwels, J., Ramasubba Reddy, M.: Multichannel EEG compression: wavelet based image and volumetric coding approach. IEEE J. Biomed. Health Inform. 17(1), 113–120 (2013)CrossRefGoogle Scholar
  15. 15.
    Abdullah, K., Kamarudin, S.I., Hussin, N.F., Jarrot, S.P.W., Ismail, A.F.: Impulsive noise effects on DWT-OFDM versus FFT-OFDM. In: IEEE 17th Asia-Pacific Conference on Communications, Sabah, Malaysia, pp. 488–491 (2011)Google Scholar
  16. 16.
    Agrawal, Sanjay, Mohanty, Soumya R., Agarwal, Vineeta: Harmonics and inter harmonics estimation of DFIG based standalone wind power system by parametric techniques. Int. J. Electr. Power Energy Syst. 67(1), 52–65 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Electrical EngineeringVIT UniversityVelloreIndia

Personalised recommendations