Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Data-Driven Dynamic State Estimation of Photovoltaic Systems via Sparse Regression Unscented Kalman Filter (2404.18305v1)

Published 28 Apr 2024 in cs.SY and eess.SY

Abstract: Dynamic state estimation (DSE) is vital in modern power systems with numerous inverter-based distributed energy resources including solar and wind, ensuring real-time accuracy for tracking system variables and optimizing grid stability. This paper proposes a data-driven DSE approach designed for photovoltaic (PV) energy conversion systems (single stage and two stage) that are subjected to both process and measurement noise. The proposed framework follows a two-phase methodology encompassing data-driven model identification" andstate-estimation." In the initial model identification phase, state feedback is gathered to elucidate the dynamics of the photovoltaic systems using nonlinear sparse regression technique. Following the identification of the PV dynamics, the nonlinear data-driven model will be utilized to estimate the dynamics of the PV system for monitoring and protection purposes. To account for incomplete measurements, inherent uncertainties, and noise, we employ an ``unscented Kalman filter," which facilitates state estimation by processing the noisy output data. Ultimately, the paper substantiates the efficacy of the proposed sparse regression-based unscented Kalman filter through simulation results, providing a comparative analysis with a physics-based DSE.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (36)
  1. E. Roman, R. Alonso, P. Ibañez, S. Elorduizapatarietxe, and D. Goitia, “Intelligent pv module for grid-connected pv systems,” IEEE Transactions on Industrial electronics, vol. 53, no. 4, pp. 1066–1073, 2006.
  2. S. Meliopoulos, G. J. Cokkinides, P. Myrda, E. Farantatos, R. Elmoudi, B. Fardanesh, G. Stefopoulos, C. Black, and P. Panciatici, “Dynamic estimation-based protection and hidden failure detection and identification: Inverter-dominated power systems,” IEEE Power and Energy Magazine, vol. 21, no. 1, pp. 59–72, 2023.
  3. X. Fan, Y. Chen, R. Wang, J. Luo, J. Wang, and D. Cao, “Integrated energy microgrid economic dispatch optimization model based on information-gap decision theory,” Energies, vol. 16, no. 8, p. 3314, 2023.
  4. J. Zhao and L. Mili, “A framework for robust hybrid state estimation with unknown measurement noise statistics,” IEEE Transactions on Industrial Informatics, vol. 14, no. 5, pp. 1866–1875, 2017.
  5. Y. Wang, Z. Yang, Y. Wang, Z. Li, V. Dinavahi, and J. Liang, “Resilient dynamic state estimation for power system using cauchy-kernel-based maximum correntropy cubature kalman filter,” IEEE Transactions on Instrumentation and Measurement, 2023.
  6. J. Zhao, A. Gómez-Expósito, M. Netto, L. Mili, A. Abur, V. Terzija, I. Kamwa, B. Pal, A. K. Singh, J. Qi et al., “Power system dynamic state estimation: Motivations, definitions, methodologies, and future work,” IEEE Transactions on Power Systems, vol. 34, no. 4, pp. 3188–3198, 2019.
  7. H. Liu, F. Hu, J. Su, X. Wei, and R. Qin, “Comparisons on kalman-filter-based dynamic state estimation algorithms of power systems,” Ieee Access, vol. 8, pp. 51 035–51 043, 2020.
  8. E. Ghahremani and I. Kamwa, “Dynamic state estimation in power system by applying the extended kalman filter with unknown inputs to phasor measurements,” IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 2556–2566, 2011.
  9. G. Valverde and V. Terzija, “Unscented kalman filter for power system dynamic state estimation,” IET generation, transmission & distribution, vol. 5, no. 1, pp. 29–37, 2011.
  10. J. Qi, K. Sun, J. Wang, and H. Liu, “Dynamic state estimation for multi-machine power system by unscented kalman filter with enhanced numerical stability,” IEEE Transactions on Smart Grid, vol. 9, no. 2, pp. 1184–1196, 2016.
  11. E. Ghahremani and I. Kamwa, “Online state estimation of a synchronous generator using unscented kalman filter from phasor measurements units,” IEEE Transactions on Energy Conversion, vol. 26, no. 4, pp. 1099–1108, 2011.
  12. R. Mohammadrezaee, J. Ghaisari, G. Yousefi, and M. Kamali, “Dynamic state estimation of smart distribution grids using compressed measurements,” IEEE Transactions on Smart Grid, vol. 12, no. 5, pp. 4535–4542, 2021.
  13. P. Marchi, P. G. Estevez, F. Messina, and C. G. Galarza, “Loss of excitation detection in synchronous generators based on dynamic state estimation,” IEEE Transactions on Energy Conversion, vol. 35, no. 3, pp. 1606–1616, 2020.
  14. A. Mokaribolhassan, G. Nourbakhsh, G. Ledwich, A. Arefi, and M. Shafiei, “Distribution system state estimation using pv separation strategy in lv feeders with high levels of unmonitored pv generation,” IEEE Systems Journal, vol. 17, no. 1, pp. 684–695, 2022.
  15. J. Khazaei, Z. Tu, and W. Liu, “Small-signal modeling and analysis of virtual inertia-based pv systems,” IEEE Transactions on Energy Conversion, vol. 35, no. 2, pp. 1129–1138, 2020.
  16. G. Kandaperumal, K. P. Schneider, and A. K. Srivastava, “A data-driven algorithm for enabling delay tolerance in resilient microgrid controls using dynamic mode decomposition,” IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 2500–2510, 2022.
  17. J. Bedi and D. Toshniwal, “Empirical mode decomposition based deep learning for electricity demand forecasting,” IEEE access, vol. 6, pp. 49 144–49 156, 2018.
  18. B. Huang and J. Wang, “Applications of physics-informed neural networks in power systems-a review,” IEEE Transactions on Power Systems, 2022.
  19. M. Budišić, R. Mohr, and I. Mezić, “Applied koopmanism,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 22, no. 4, p. 047510, 2012.
  20. A. Mauroy and J. Goncalves, “Koopman-based lifting techniques for nonlinear systems identification,” IEEE Transactions on Automatic Control, vol. 65, no. 6, pp. 2550–2565, 2019.
  21. M. C. Di Piazza, M. Luna, and G. Vitale, “Dynamic pv model parameter identification by least-squares regression,” IEEE Journal of Photovoltaics, vol. 3, no. 2, pp. 799–806, 2013.
  22. A. Alqahtani, M. Alsaffar, M. El-Sayed, B. Alajmi et al., “Data-driven photovoltaic system modeling based on nonlinear system identification,” International Journal of Photoenergy, vol. 2016, 2016.
  23. A. M. Dizqah, A. Maheri, and K. Busawon, “An accurate method for the pv model identification based on a genetic algorithm and the interior-point method,” Renewable Energy, vol. 72, pp. 212–222, 2014.
  24. S. L. Brunton, J. L. Proctor, and J. N. Kutz, “Discovering governing equations from data by sparse identification of nonlinear dynamical systems,” Proceedings of the national academy of sciences, vol. 113, no. 15, pp. 3932–3937, 2016.
  25. Brunton, Steven L and Proctor, Joshua L and Kutz, J Nathan, “Sparse identification of nonlinear dynamics with control (sindyc),” IFAC-PapersOnLine, vol. 49, no. 18, pp. 710–715, 2016.
  26. U. Fasel, E. Kaiser, J. N. Kutz, B. W. Brunton, and S. L. Brunton, “Sindy with control: A tutorial,” in 2021 60th IEEE Conference on Decision and Control (CDC).   IEEE, 2021, pp. 16–21.
  27. L. Dang, B. Chen, S. Wang, W. Ma, and P. Ren, “Robust power system state estimation with minimum error entropy unscented kalman filter,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 11, pp. 8797–8808, 2020.
  28. A. Surana and A. Banaszuk, “Linear observer synthesis for nonlinear systems using koopman operator framework,” IFAC-PapersOnLine, vol. 49, no. 18, pp. 716–723, 2016.
  29. K. Wang, M. Liu, W. He, C. Zuo, and F. Wang, “Koopman kalman particle filter for dynamic state estimation of distribution system,” IEEE Access, vol. 10, pp. 111 688–111 703, 2022.
  30. M. Netto and L. Mili, “A robust data-driven koopman kalman filter for power systems dynamic state estimation,” IEEE Transactions on Power Systems, vol. 33, no. 6, pp. 7228–7237, 2018.
  31. J. Zhao, M. Netto, and L. Mili, “A robust iterated extended kalman filter for power system dynamic state estimation,” IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 3205–3216, 2016.
  32. J. Khazaei and R. S. Blum, “Model-free distributed control of dynamical systems,” International Journal of Information and Communication Engineering, vol. 16, no. 10, pp. 475–480, 2022.
  33. R. E. Kalman, “Lectures on controllability and observability,” in CENTRO INTERN. MAT. ESTIVO SEMINAR ON CONTROLLABILITY AND OBSERVABILITY, no. NASA-CR-113869, 1968.
  34. E. A. Wan and R. Van Der Merwe, “The unscented kalman filter for nonlinear estimation,” in Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No. 00EX373).   Ieee, 2000, pp. 153–158.
  35. S. J. Julier and J. K. Uhlmann, “Unscented filtering and nonlinear estimation,” Proceedings of the IEEE, vol. 92, no. 3, pp. 401–422, 2004.
  36. G. Welch, G. Bishop et al., “An introduction to the kalman filter,” 1995.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now