e-ISSN 2231-8542
ISSN 1511-3701

Home / Regular Issue / JTAS Vol. 30 (3) Jul. 2022 / JST-3337-2021


A Hybrid Technique for Analysis of Low-Frequency Oscillation in Power System

Abhinav Pathak and Ratnesh Gupta

Pertanika Journal of Tropical Agricultural Science, Volume 30, Issue 3, July 2022


Keywords: Attenuation factor, damping ratio, Prony algorithm, stability, synthetic signal

Published on: 25 May 2022

Estimating the low-frequency oscillation in an interconnected power system is the most important requirement to keep the power system in a stable operating condition. This research work deals with a hybrid robust and accurate approach using a combination of Estimation of signal parameters via rotational invariant techniques (ESPRIT) and Prony algorithm to extract the low-frequency oscillatory modes present in the power system. The observation inspires the hybrid method that the true modes of the signal are present in any signal processing technique (for example, Prony algorithm) along with other fictitious modes regardless of the order of the power system. Moreover, this research obtained true modes by calculating Euclidean distance and applying the threshold value concept. The proposed technique is tested with different noise conditions and varying sampling rates of Phasor Measurement Unit (PMU) to check the proposed hybrid technique’s robustness compared to Prony and the multiple ESPRIT method. Finally, the proposed method is applied to the real signal obtained from the Western Electricity Coordinating Council (WECC) network, and it estimates accurate and precise parameters compared to other methods. The accuracy for estimation of frequency and attenuation factor is calculated for the three-mode synthetic signal at a noise level of 10dB by the hybrid algorithm, multiple ESPRIT, and Prony algorithm, which shows that hybrid algorithm has minimum percentage error. Thus, the proposed hybrid algorithm accurately estimates the parameters of low-frequency oscillation as compared to other existing methods without involving any fictitious modes.

  • Amono, M., Watanabe, M., & Banjo, M. (1999). Self-testing and self-tuning of power system stabilizers using Prony analysis. In IEEE Power Engineering Society. Winter Meeting (Cat. No.99 CH36233) (Vol 1, pp. 655-660). IEEE Publishing.

  • Avdakovic, S., Nuhanovic, A., Kusljugic, M., & Music, M. (2012). Wavelet transform applications in power system dynamics. Electric Power Systems Research, 83, 237-245.

  • Browne, T. J., Vittal, V., Heydt, G. T., & Messina, A. R. (2008). A comparative assessment of two techniques for modal identification from power system measurements. In IEEE Transactions on Power Systems, (Vol. 23, pp. 1408-1415). IEEE Publishing.

  • Girgis, A. A., & Ham, F. M. (1980). A quantitative study of pitfalls in the FFT. In IEEE Transactions on Aerospace and Electronic Systems, (Vol. 4, pp. 434-439). IEEE Publishing.

  • Glickman, M., O’Shea, P., & Ledwich, G. (2007). Estimation of modal damping in power networks. In IEEE Transactions on Power Systems, (Vol. 22, pp. 1340-350). IEEE Publishing.

  • Grant, L. L., & Crow, M. L. (2011). Comparison of matrix pencil and prony methods for power system modal analysis of noisy signals. In 2011 North American Power Symposium (pp.1-7). IEEE Publishing.

  • Hauer, J. F. (1991). Application of Prony analysis to the determination of modal content and equivalent models for measured power system response. In IEEE Transactions on Power Systems, (Vol. 6, pp.1062-1068). IEEE Publishing.

  • Hauer, J. F., Demeure, C. J., & Scharf, L. L. (1990). Initial results in Prony analysis of power system response signals. In IEEE Transactions on Power Systems, (Vol. 5, pp. 80-89). IEEE Publishing.

  • Hua, Y., & Sarkar, T. K. (1990). Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise. In IEEE Transactions on Acoustics, Speech, and Signal Processing, (Vol. 38, pp. 814-824). IEEE Publishing.

  • Kang, P., & Ledwich, G. (1999). Estimating power system modal parameters using wavelets. ISSPA ’99. Proceedings of the Fifth International Symposium on Signal Processing and Its Applications (IEEE Cat. No.99EX359) (Vol. 2, pp. 563-566). IEEE Publishing.

  • Korba, P., Larsson, M., & Rehtanz, C. (2003). Detection of oscillations in power systems using Kalman filtering techniques. In Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003 (Vol.1, pp. 183-188). IEEE Publishing.

  • Kundur, P. (1994). Power system stability and control. Tata Mc-Graw Hill Co.

  • Laila, D. S., Messina, A. R., & Pal, B. C. (2009). A refined Hilbert-Huang transform with applications to inter-area oscillation monitoring. In IEEE Transactions on Power Systems, (pp. 610-620). IEEE Publishing.

  • Philip, J. G., & Jain, T. (2018a). Analysis of low frequency oscillations in power system using EMO ESPRIT. International Journal of Electrical Power & Energy Systems, 95, 499-506.

  • Philip, J. G., & Jain, T. (2018b). Estimation of modal parameters of low frequency oscillations in power system using Hankels total least square method. In 2018 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), (pp. 764-769). IEEE Publishing.

  • Pierre, J. W., Trudnowski, D. J., & Donnelly, M. K. (1997). Initial results in electromechanical mode identification from ambient data. In IEEE Transactions on Power Systems (Vol. 12, pp. 1245-1251). IEEE Publishing.

  • Qi, L., Qian, L., Woodruff, S., & Cartes, D. (2007). Prony analysis for power system transient harmonics. EURASIP Journal on Advances in Signal Processing, 2007, Article 48406.

  • Rai, S., Tripathy, P., & Nayak, S. K. (2014). A robust TLS-ESPIRIT method using covariance approach for identification of low-frequency oscillatory mode in power systems. In 2014 Eighteenth National Power Systems Conference (NPSC) (pp. 1-6). IEEE Publishing.

  • Rai, S., Lalani, D., Nayak, S. K. K., Jacob, T., & Tripathy, P. (2016). Estimation of low-frequency modes in power system using robust modified Prony. IET Generation, Transmission & Distribution, 10(6), 1401-1409.

  • Rueda, J. L., Juarez, C. A., & Erlich, I. (2011). Wavelet-based analysis of power system low-frequency electromechanical oscillations. In IEEE Transactions on Power Systems (Vol. 26, pp. 1733-1743). IEEE Publishing.

  • Trentini, R., Kutzner, R., Hofmann, L., Oliveira, J. de, & Nied, A. (2019). On the electromechanical energy approach: a novel modeling method for power systems stability studies. In IEEE Transactions on Power Systems (Vol. 34, pp. 1771-1779). IEEE Publishing.

  • Tripathy, P., Srivastava, S. C., & Singh, S. N. (2011). A modified TLS-ESPRIT-based method for low-frequency mode identification in power systems utilizing synchrophasor measurements. In IEEE Transactions on Power Systems (Vol. 26, pp. 719-727). IEEE Publishing.

  • Trudnowski, D. I. (1994). Order reduction of large-scale linear oscillatory system models. In IEEE Transactions on Power Systems (Vol. 9, pp. 451-458). IEEE Publishing.

  • Trudnowski, D. J., Johnson, J. M., & Hauer, J. F. (1999). Making Prony analysis more accurate using multiple signals. In IEEE Transactions on Power Systems (Vol. 14, pp. 226-231). IEEE Publishing.

  • Wadduwage, D. P., Annakkage, U. D., & Narendra, K. (2015). Identification of dominant low-frequency modes in ring-down oscillations using multiple Prony models. IET Generation, Transmission & Distribution, 9(15), 2206-2214.

  • Wang, L., & Semlyen, A. (1990). Application of sparse eigenvalue techniques to the small signal stability analysis of large power systems. IEEE Transactions on Power Systems (Vol. 5, pp. 635-642). IEEE Publishing.

  • Wang, X., Tang, F., Wang, X., & Zhang, P. (2014). Estimation of electromechanical modes under ambient condition via random decrement technique and TLS-ESPRIT algorithm. In 2014 International Conference on Power System Technology (pp. 588-593). IEEE Publishing.

  • Wies, R. W., Pierre, J. W., & Trudnowski, D. J. (2003). Use of ARMA block processing for estimating stationary low-frequency electromechanical modes of power systems. IEEE Transactions on Power Systems (Vol. 18, pp. 167-173). IEEE Publishing.

  • Xie, X., Zhang, S., Xiao, J., Wu, J., & Pu, Y. (2005). Small signal stability assessment with online eigenvalue identification based on wide-area measurement system. 2005 IEEE/PES Transmission Distribution Conference Exposition: Asia and Pacific, (pp 1-5). IEEE Publishing.

  • Zhang, S., Xie, X., & Wu, J. (2008). WAMS-based detection and early-warning of low-frequency oscillations in large-scale power systems. Electric Power Systems Research, 78(5), 897-906.

  • Zhou, N., Huang, Z., Tuffner, F., Pierre, J., & Jin, S. (2010). Automatic implementation of Prony analysis for electromechanical mode identification from phasor measurements. IEEE PES General Meeting (pp. 1-8). IEEE Publishing.

  • Zhou, N., Trudnowski, D. J., Pierre, J. W., & Mittelstadt, W. A. (2008). Electromechanical mode online estimation using regularized robust RLS methods. In IEEE Transactions on Power Systems (Vol. 23, pp. 1670-1680). IEEE Publishing.

ISSN 1511-3701

e-ISSN 2231-8542

Article ID


Download Full Article PDF

Share this article

Recent Articles