Home Technology Design of novel UPFC based damping controller for solar PV integrated power system using arithmetic optimization algorithm
Article
Licensed
Unlicensed Requires Authentication

Design of novel UPFC based damping controller for solar PV integrated power system using arithmetic optimization algorithm

  • Sankalpa Bohidar , Ranjan Kumar Mallick ORCID logo EMAIL logo , Pravati Nayak , Sairam Mishra ORCID logo , Narayan Nahak , Gayadhar Panda and Pramod Kumar Gouda
Published/Copyright: June 18, 2024

Abstract

Integrating renewable energy sources like solar power into traditional power systems poses challenges. One such challenge is the effect of renewable power plants, which use power electronics, on the grid’s stability. Specifically, these plants can impact small-signal stability by either damping or exacerbating low-frequency oscillations. This paper introduces a novel Unified Power Flow Controller (UPFC) based damping controller specifically designed for Solar Photovoltaic (PV) integrated power systems. It employs an Arithmetic Optimization Algorithm (AOA) to optimize the UPFC damping controller parameters and mitigate low-frequency oscillations in the power system. The objective function minimizes the Integral Time Absolute Error (ITAE) of speed deviations under varying loading conditions. The proposed technique is utilized simultaneously to control the modulation index of series and phase angle of shunt converters of UPFC. The MATLAB/simulation results obtained effectively from the proposed technique which is actualized and identify both detrimental and beneficial impacts of increased PV penetration for small signal stability performance. The study reveals both the small-signal stability of the system and its response to large disturbances that alter the active power balance and frequency stability. The results of the analysis demonstrated with single and multimachine environment by comparing with the other optimizations like PSO, DE, DE-PSO and GWO, the proposed one is effective for damping out the oscillations. The effectiveness of the proposed damping controller is further confirmed through real-time validation using the OPAL-RT setup.


Corresponding author: Ranjan Kumar Mallick, Department of Electrical and Electronics Engineering, SOA University, Bhubaneswar, Odisha 751030, India, E-mail:

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: Not applicable.

Appendix

(All the data are in per unit other than constants).

I. Single machine infinite bus test system data

C dc = 1, H = 4 MJ/MVA, Ka = 100, Ta = 0.01, T d0 = 5.044 s, D = 0, δ 0 = 47.13°, V b = 1, V dc = 2, V t = 1, X B = X E = 0.1, X BV = 0.3, X d = 1, X E = 0.1, X d = 0.3, X q = 0.6, Xe = 0.5.

II. Multi-machine system data

H 2 = 20, H 3 = 11.8, D 2 = D 3 = 0, T d02 = 7.5 s, T d03 = 4.7 s, T dc = 0.01, K dc = 5, X q2 = 0.16, X q3 = 0.33, X d2 = 0.19, X d3 = 0.41, X d2 = 0.076, T A2 = 0.01, K A2 = 100, K A3 = 20, T A2 = 0.01, Z 13 = j0.6 (double lines), Z 23 = j0.1, L 3 = 0.8 − j1.253, V 3 = 1 < 0°, V 2 = 1 < 5°.

References

1. Zarifakis, M, Coffey, WT, Kalmykov, YP, Titov, SV, Byrne, DJ, Carrig, SJ. Active damping of power oscillations following frequency changes in low inertia power systems. IEEE Trans Power Syst 2019;34:4984–92. https://doi.org/10.1109/tpwrs.2019.2911845.Search in Google Scholar

2. Favuzza, S, Navarro Navia, MS, Musca, R, Riva Sanseverino, E, Zizzo, G, Doan Van, B, et al.. Impact of RES penetration on the frequency dynamics of the 500 kV vietnamese power system. In: 2019 8th international conference on renewable energy research and applications (ICRERA). Brasov, Romania: IEEE; 2019:668–72 pp.10.1109/ICRERA47325.2019.8996862Search in Google Scholar

3. Yin, H, Dereschkewitz, M, Wagenitz, D, Dieckerhoff, S. A versatile test bench for grid integration investigations of back-to-back wind energy conversion systems. In: 2014 international conference on renewable energy research and application (ICRERA). Milwaukee, WI; 2014:695–700 pp.10.1109/ICRERA.2014.7016475Search in Google Scholar

4. Shi, LB, Wang, C, Yao, LZ, Wang, LM, Ni, YX. Analysis of impact of grid-connected wind power on small signal stability. Wind Energy 2011;14:518–37. https://doi.org/10.1002/we.440.Search in Google Scholar

5. Banna, HU, Luna, A, Ying, S, Ghorbani, H, Rodriguez, P. Impacts of wind energy in-feed on power system small signal stability. In: 2014 international conference on renewable energy research and application (ICRERA). Milwaukee, WI; 2014:615–22 pp.10.1109/ICRERA.2014.7016459Search in Google Scholar

6. Slootweg, JG, Kling, WL. The impact of large scale wind power generation on power system oscillations. Elec Power Syst Res 2003;67:9–20. https://doi.org/10.1016/s0378-7796(03)00089-0.Search in Google Scholar

7. Liu, W, Ge, R, Li, H, Ge, J. Impact of large-scale wind power integration on small signal stability based on stability region boundary. Sustainability 2014;6:7921–44. https://doi.org/10.3390/su6117921.Search in Google Scholar

8. Bhushan, R, Chatterjee, K. Effects of parameter variation in DFIG-based grid connected system with a FACTS device for small-signal stability analysis. IET Gener Transm Distrib 2017;11:2762–77. https://doi.org/10.1049/iet-gtd.2016.1329.Search in Google Scholar

9. Das, UK, Tey, KS, Seyedmahmoudian, M, Mekhilef, S, IdnaIdris, MY, Van Deventer, W, et al.. Forecasting of photovoltaic power generation and model optimization: a review. Renew Sustain Energy Rev 2018;81:912–28. https://doi.org/10.1016/j.rser.2017.08.017.Search in Google Scholar

10. Tan, YT, Kirschen, DS, Jenkins, N. A model of PV generation suitable for stability analysis. IEEE Trans Energy Convers 2004;19:748–755. https://doi.org/10.1109/tec.2004.827707.Search in Google Scholar

11. Xia, Y, Peng, Y, Yang, P, Li, Y, Wei, W. Different influence of grid impedance on low- and high-frequency stability of PV generators. IEEE Trans Ind Electron 2019;66:8498–508. https://doi.org/10.1109/tie.2019.2891459.Search in Google Scholar

12. Kajiwara, K, Matsui, N, Kurokawa, F. A new MPPT control for solar panel under bus voltage fluctuation. In: 2017 IEEE 6th international conference on renewable energy research and applications (ICRERA). San Diego, CA; 2017:1047–50 pp.10.1109/ICRERA.2017.8191217Search in Google Scholar

13. Güler, N, Irmak, E. MPPT based model predictive control of grid connected inverter for PV systems. In: 2019 8th international conference on renewable energy research and applications (ICRERA). Brasov, Romania; 2019:982–6 pp.10.1109/ICRERA47325.2019.8997105Search in Google Scholar

14. Matsuo, H, Kurokawa, F. Novel solar cell power supply system using bidirectional dc-dc converter. In: Proc. IEEE power electron. spec. conf. (PESC). Cambridge, MA, Jun; 1982:14–19 pp.10.1109/PESC.1982.7072390Search in Google Scholar

15. Du, W, Wang, HF, Dunn, R. Power system small-signal oscillation stability as affected by large-scale PV penetration. In: International conference on sustainable power generation and supply, SUPERGEN-2009. China; 2009.10.1109/SUPERGEN.2009.5348073Search in Google Scholar

16. Salas-Mora, VM, Richmond-Navarro, G. Safety design of a hybrid wind-solar energy system for rural remote areas in Costa Rica. Int J Renew Energy Resour 2020;10:33–44.Search in Google Scholar

17. Lee, DJ, Wang, L. Small-signal stability analysis of an autonomous hybrid renewable energy power generation/energy storage system part I: time-domain simulations. IEEE Trans Energy Convers 2008;23:311–20. https://doi.org/10.1109/tec.2007.914309.Search in Google Scholar

18. Panda, S, Swain, SC, Rautray, PK, Malik, RK, Panda, G. Design and analysis of SSSC-based supplementary damping controller. Simulat Model Pract Theor 2010;18:1199–213. https://doi.org/10.1016/j.simpat.2010.04.007.Search in Google Scholar

19. Panda, S. Multi-objective evolutionary algorithm for SSSC-based controller design. Elec Power Syst Res 2009;79:937–44. https://doi.org/10.1016/j.epsr.2008.12.004.Search in Google Scholar

20. Panda, S. Robust coordinated design of multiple and multi-type damping controller using differential evolution algorithm. Int J Electr Power Energy Syst 2011;33:1018–30. https://doi.org/10.1016/j.ijepes.2011.01.019.Search in Google Scholar

21. Guo, C, Jiang, W, Zhao, C. Small-signal instability and supplementary coordinated damping-control of LCC-HVDC system with STATCOM under weak AC grid conditions. Int J Electr Power Energy Syst 2019;104:246–54. https://doi.org/10.1016/j.ijepes.2018.06.055.Search in Google Scholar

22. Abd-Elazim, SM, Ali, ES. Imperialist competitive algorithm for optimal STATCOM design in a multimachine power system. Int J Electr Power Energy Syst 2016;76:136–46. https://doi.org/10.1016/j.ijepes.2015.09.004.Search in Google Scholar

23. Abido, MA. Analysis and assessment of STATCOM-based damping stabilizers for power system stability enhancement. Elec Power Syst Res 2005;73:177–85. https://doi.org/10.1016/j.epsr.2004.08.002.Search in Google Scholar

24. Wang, HF. A unified model for the analysis of FACTS devices in damping power system oscillations – part III: unified power flow controller. IEEE Trans Power Deliv 2000;15:978–83.10.1109/61.871362Search in Google Scholar

25. Tambey, N, Kothari, ML. Damping of power system oscillations with unified power flow controller (UPFC). IEE Proc Gener Trans Distrib 2003;150:129–40. https://doi.org/10.1049/ip-gtd:20030114.10.1049/ip-gtd:20030114Search in Google Scholar

26. Taher, SA, Hemmati, R, Abdolalipour, A, Akbari, S. Comparison of different robust control methods in the design of decentralized UPFC controllers. Int J Electr Power Energy Syst 2012;43:173–84. https://doi.org/10.1016/j.ijepes.2012.04.026.Search in Google Scholar

27. Kumar, BV, Srikanth, NV. A hybrid approach for optimal location and capacity of UPFC to improve the dynamic stability of the power system. Appl Soft Comput J 2016;52:974–86. https://doi.org/10.1016/j.asoc.2016.09.031.Search in Google Scholar

28. Pandey, RK, Singh, NK. UPFC control parameter identification for effective power oscillation damping. Electr Power Energy Syst 2009;31:269–76. https://doi.org/10.1016/j.ijepes.2009.03.002.Search in Google Scholar

29. Eslami, M, Shareef, H, Taha, MR, Khajehzadeh, M. Adaptive particle swarm optimization for simultaneous design of UPFC damping controllers. Int J Electr Power Energy Syst 2014;57:116–28. https://doi.org/10.1016/j.ijepes.2013.11.034.Search in Google Scholar

30. Shaheen, HI, Rashed, GI, Cheng, SJ. Optimal location and parameter setting of UPFC for enhancing power system security based on differential evolution algorithm. Int J Electr Power Energy Syst 2011;33:94–105. https://doi.org/10.1016/j.ijepes.2010.06.023.Search in Google Scholar

31. Mallick, RK, Nahak, N. Hybrid differential evolution particle swarm optimization (DE-PSO) algorithm for optimization of unified power flow controller parameters. In: IEEE, UPCON-IIT BHU; 2016:635–40 pp.10.1109/UPCON.2016.7894729Search in Google Scholar

32. Khadanga, RK, Satapathy, JK. A new hybrid GA–GSA algorithm for tuning damping controller parameters for a unified power flow controller. Electr Power Energy Syst 2015;73:1060–9. https://doi.org/10.1016/j.ijepes.2015.07.016.Search in Google Scholar

33. Nahak, N, Mallick, RK. Damping of power system oscillations by a novel DE-GWO optimized dual UPFC controller. Eng Sci Technol Int J 2017;20:1275–84. https://doi.org/10.1016/j.jestch.2017.09.001.Search in Google Scholar

34. Nahak, N, Mallick, RK. Damping of oscillations in multi machine power system by PSO-GWO optimised dual UPFC-based controller. Int J Adv Intell Paradigms 2023;26:304–22. https://doi.org/10.1504/ijaip.2023.10061398.Search in Google Scholar

35. Wang, T, Jin, M, Li, Y, Wang, J, Wang, Z, Huang, S. Adaptive damping control scheme for wind grid-connected power systems with virtual inertia control. IEEE Trans Power Syst 2022;37:3902–12. https://doi.org/10.1109/tpwrs.2021.3140086.Search in Google Scholar

36. Abualigah, L, Diabat, A, Mirjalili, S, Abd Elaziz, M, Gandomi, AH. The arithmeticm optimization algorithm. Comput Methods Appl Mech Eng 2021;376:113609. https://doi.org/10.1016/j.cma.2020.113609.Search in Google Scholar

Received: 2024-05-13
Accepted: 2024-05-16
Published Online: 2024-06-18

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 15.3.2026 from https://www.degruyterbrill.com/document/doi/10.1515/ijeeps-2024-0138/html
Scroll to top button