Power Spectral Density based Identification of Low frequency ‎Oscillations in Multimachine Power system

Document Type : Research paper


Department of Electrical and Electroinics Engineering, NMAM Institute of Technology Nitte, Karkala,India


The paper presents a Fast Fourier Transform (FFT) based Power Spectral Density (PSD) filter for denoising the PMU signal received from the smart power system network to identify the low frequency Oscillation modes (LFO). Small disturbances are introduced during normal operation of power system causes low frequency oscillations and may hinder the power system transfer capabilities of a system. The traditional signal processing method cannot extract the information from ambient signals effectively during noisy measurement. In this paper, the performance of the Prony analysis with reduced sampling rate is analysed for the PMU data with noiseless and noise environment. It is observed that, the performance of the Prony approach is not satisfactory under noisy measurement data.  In the present work FFT-PSD is used to denoise the noisy measurement signal and identify the nature of the decrement factor of the low frequency oscillatory modes. The accuracy of the estimated decrement factors of modes are verified with eigenvalues to validate the proposed method. The performance of proposed method is compared with signal processing method for IEEE New England power system and found effective and suitable during noisy PMU measurements.


  1. Kundur, “Power system stability and control”, New York, NY, USA, McGraw-Hill, pp. 669-1161, 1994.
  2. R. Padiyar, “Power system dynamics-Stability and Control”, Hyderabad, India, BS Publications, 2nd edition, 2002.
  3. Messina and V. Vittal “Extraction of dynamic patterns from wide-area measurements using empirical orthogonal functions”, IEEE Trans. Power Syst., vol.22, pp. 682–692, 2007.
  4. Hauer, CJ. Demeure and Li. Scharf “Initial results in prony analysis of power system response signals”, IEEE Trans. Power Syst., vol.5, pp. 80-89, 2009.
  5. Zhou, JW. Pierr and D. Trudnowski, “A stepwise regression method for estimating dominant electromechanical modes”, IEEE Trans. Power Syst., vol.27, pp. 1051–1059, 2012.
  6. Jian, X. Lin, M.A. Mohammad and T. Jin, “An adaptive matrix pencil algorithm based-wavelet soft-threshold denoising for analysis of low frequency oscillation in power system”, IEEE Access, vol. 8, pp. 7144-255, 2020.
  7. Hujjar and Suman, “Power system oscillation modes identifications from wide area frequency measurement system”, Int. Conf. Powercon, 2012.
  8. Flandrin P, G. Rilling and P. Goncalves, “Empirical mode decomposition as a filter bank”, IEEE Signal Process Lett, vol.11, pp. 112–4, 2004.
  9. Li, T. Jiang and H. Cui, “An eigen realization algorithm based data driven approach for extracting electromechnaical oscillations dynamic pattern from synchro phasor measurement in bulk power grids”, Int. J. Elect. Power Energy Syst., vol.116, pp. 1-11, 2020.
  10. wu, G. Gao, C. Cui, “ Improved wavelet denoising by non-convex sparse regularization under double wavelet domains”, IEEE Access, vol.7, pp. 30659-30671, 2019.
  11. Ray,” Power system low frequency oscillation mode estimation using wide area measurement systems”, Eng. Sci. Tech. Int. J., vol.20, pp. 598-615, 2017.
  12. Chen, X. Lin, MA. Mohammad and T. Jin, “An Adaptive TLS-ESPRIT algorithm based on an S-G filter for analysis of low frequency oscillation in wide area measurement systems”, IEEE Access vol.7, pp. 47644-47654, 2019.
  13. Liu, M. Shiu and C. Chen,” ECG Signal Denoising and Reconstruction Based on Basis Pursuit”, MDPI, Appl. Energy, vol.11, pp-1-15, 2021.
  14. liu, S. Lin and C. Chen, “Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method”, MDPI, Appl. Sci., vol.11, pp. 1-15, 2021.
  15. Kazaei, LF Anf, W.Jiang and Durgesh, “Distributed Prony analysis for real-world PMU data”, Electr. Power Syst. Res., vol.133, pp. 113-120, 2016.
  16. Chen, X. Li and MA. Mohammed, “An adaptive matrix pencil algorithm based-wavelet soft-threshold denoising for analysis of low frequency oscillation in power systems”, IEEE Access, vol.8, pp.7244 – 7255, 2020.
  17. Satheesh and S. Rajan, “Dominant electromechanical oscillation mode identification using modified variational mode decomposition”, Arabian J. Sci. Eng., vol.46, pp. 10007–10021, 2021.
  18. Agarmohammadi and SM. Tabandesh, “A new approach for online coherency identification based on correlation characteristics”, Int. J. Elct. Power Energy Syst, vol.83, pp. 470-484, 2016.
  19. Li, HT Cui, T. Jiang, Y. Xu, HJ. Jia and Li. FX, “Multichannel continuous wavelet transform approach to estimate electromechanical oscillation modes, mode shapes and coherent groups from synchro phasors in bulk power grids”, Int. J. Elect Power Energy Syst., vol. 96, pp. 222–237, 2018.
  20. Lardiès,“Modal parameter identification by an iterative approach and by the state space model, mechanical systems and signal processing”, Mech. Syst. Signal Proc., vol. 95, pp. 239–251, 2017.
  21. Shetty and N. Prabhu, “Performance analysis of data-driven techniques for detection and identification of low frequency oscillations in multimachine power system”, IEEE Access, vol. 9, pp. 133416-133437, 2021.
  22. Shetty and N. Prabhu, “Low frequency oscillation detection in smart power system using refined prony analysis for optimal allocation of supplementary modulation controller”, IEEE Conf. Proc. ICOEI, 2019.
  23. Proakis and DG. Manolakis, “Digital Signal Processing”, Dorling Kindersley Pvt Ltd, India, 4th edition, pp. 449-1030, 2014.
  24. Sanchez-Gasca, “Identification of Electromechanical Modes in Power Systems,” IEEE PES Resource. Center, IEEE PES Tech. Report. TR-15, pp. 1–282, 2012.
  25. Casiano, “Extracting damping ratio from dynamic data and numerical solution”, NASA/TM, pp. 218-227, 2016.
  26. Li, T. Jiang and H. Cui, “Prioritization of PMU Location and Signal Selection for Monitoring Critical Power System Oscillations”, IEEE Trans. Power Syst., vol. 33, pp. 3919- 3929, 2020.
  27. Khalilinia and V. Subramaniam, “Modal analysis of ambient PMU measurements using orthogonal wavelet bases”, IEEE Trans. Smart Grid, pp. 2954-2963, 2015.
  28. Shahriyariand H. Khoshkhoo, “A deep learning-based approach for comprehensive rotor angle stability ‎assessment”, J. Oper. Autom. Power Eng., vol.10, pp. 105-115, 2022.
  29. Gorginpour, “Analytical calculation of the equivalent circuit parameters of non-salient pole large synchronous generators”, J. Oper. Autom. Power Eng., vol. 9, pp. 172-181, 2021.
Volume 11, Issue 3
October 2023
Pages 173-181
  • Receive Date: 02 January 2022
  • Revise Date: 25 February 2022
  • Accept Date: 02 May 2022
  • First Publish Date: 08 June 2022