Abstract
This paper presents stability enhancement of a test system that is connected with a Wind Farm (WF) by using Power System Stabilizer (PSS) for Synchronous Generator (SG) and Power Oscillation Damper (POD) for Static Var Compensator (SVC). This paper also proposes a coordination mechanism for the controller to effectively damp out the oscillations and make the power system more stable by considering the uncertainties. The uncertainty is considered as wind speed variation and wind power penetration and different locations. The Particle Swarm Optimization (PSO) is used to overcome the controller parameter tuning drawbacks and controller coordination. The SG rotor speed deviation is selected as an objective function with various constraints for PSO. The transient stability analysis is carried out by considering large disturbance that is a three-phase fault. The nonlinear dynamic simulation results are obtained by integrating WF and SG replacement with the same rating WF. Evaluation and analysis are performed for various cases and different combination of without and with controllers. From the simulation results, it is noticed that oscillations in the system are minimized, and stability is enhanced at the maximum level. It also observed that the capability of SG and DFIG under three-phase fault is intensified by using PSO for optimized coordinated controller parameters. The robustness and effectiveness of the proposed approaches are evaluated on the IEEE-11 bus test system.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
Doubly fed induction generator (DFIG) based wind farm
Stator resistance R s = 0.01 pu; Stator reactance X s = 0.10 pu; Rotor resistance R r = 0.01 pu. Rotor reactance X r = 0.08 pu; Magnetization reactance X m = 3 pu. Inertia constants, H m = 3 kW s/kVA; Pitch control gain = 10 pu; Time constants = 3 s. Voltage control gain K v = 10 pu; Power control time constant T e = 0.01 s. Number of poles = 4; gear box ratio = 1:89; Blade length = 75.0 m; number of blade = 3.
PI controller gain for RSC
PI controller gain for GSC
PSS for SG
SVC at Bus 08
POD for SVC
A min, B min and C min as well as A max, B max, and C max are lower and upper limits respectively.
IEEE-11 bus (two area system)
Synchronous generator data (Power rating = 900 MVA, Voltage rating = 20 kV).
Variable (p.u.) | SG1 at Bus 1 | SG2 at Bus 2 | SG3 at Bus 3 | SG4 at Bus 4 |
---|---|---|---|---|
X 1 | 0.22 | 0.22 | 0.22 | 0.22 |
R s | 0.025 | 0.025 | 0.025 | 0.025 |
X d | 0.18 | 0.18 | 0.18 | 0.18 |
|
0.33 | 0.33 | 0.33 | 0.33 |
X q | 0.19 | 0.19 | 0.19 | 0.19 |
|
0.55 | 0.55 | 0.55 | 0.55 |
|
8.0 | 8.0 | 8.0 | 8.0 |
|
0.4 | 0.4 | 0.4 | 0.4 |
H (sec) | 54 | 54 | 63 | 63 |
D | 0 | 0 | 0 | 0 |
Excitation system data.
Variable | SG1 at Bus 1 | SG2 at Bus 2 | SG3 at Bus 3 | SG4 at Bus 4 |
---|---|---|---|---|
K A | 200 | 200 | 200 | 200 |
T A (sec) | 0.001 | 0.001 | 0.001 | 0.001 |
K E | 1 | 1 | 1 | 1 |
T E (sec) | 0.314 | 0.314 | 0.314 | 0.314 |
K F | 0.063 | 0.063 | 0.063 | 0.063 |
T F (sec) | 0.35 | 0.35 | 0.35 | 0.35 |
Transmission line data.
From bus | To bus | Series resistance R (%) |
Series reactance X (%) |
Shunt susceptance B/2 (%) |
Length of transmission line (km) |
---|---|---|---|---|---|
5 | 6 | 0.5 | 5.0 | 2.1875 | 25 |
5 | 6 | 0.5 | 5.0 | 2.1875 | 25 |
6 | 7 | 0.3 | 3.0 | 0.5833 | 10 |
6 | 7 | 0.3 | 3.0 | 0.5833 | 10 |
6 | 7 | 0.3 | 3.0 | 0.5833 | 10 |
7 | 8 | 1.1 | 11.0 | 19.25 | 110 |
7 | 8 | 1.1 | 11.0 | 19.25 | 110 |
8 | 9 | 1.1 | 11.0 | 19.25 | 110 |
8 | 9 | 1.1 | 11.0 | 19.25 | 110 |
9 | 10 | 0.3 | 3.0 | 0.5833 | 10 |
9 | 10 | 0.3 | 3.0 | 0.5833 | 10 |
9 | 10 | 0.3 | 3.0 | 0.5833 | 10 |
10 | 11 | 0.5 | 5.0 | 2.1875 | 25 |
10 | 11 | 0.5 | 5.0 | 2.1875 | 25 |
Generator step-up transformer data (on Transformer MVA Base).
From bus | To bus | R (%) | X (%) | MVA base |
---|---|---|---|---|
1 | 5 | 0.015 | 15 | 900 |
2 | 6 | 0.015 | 15 | 900 |
3 | 11 | 0.015 | 15 | 900 |
4 | 10 | 0.015 | 15 | 900 |
Bus and load data.
Bus | Type (kV) | P L (MW) | Q L (MVAr) | Q c (MVAr) | P G (MW) |
---|---|---|---|---|---|
1 | 20 | – | – | – | 720 (slack bus) |
2 | 20 | – | – | – | 720 |
3 | 20 | – | – | – | 720 |
4 | 20 | – | – | – | 720 |
5 | 230 | – | – | – | – |
6 | 230 | – | – | – | – |
7 | 230 | 967 | 200 | 100 | – |
8 | 230 | – | – | – | – |
9 | 230 | 1767 | 350 | 100 | – |
10 | 230 | – | – | – | – |
11 | 230 | – | – | – | – |
References
1. He, P, Wen, F, Ledwich, G, Xue, Y. An investigation on interarea mode oscillations of interconnected power systems with integrated wind farms. Int J Electr Power Energy Syst 2016;78:148–57. https://doi.org/10.1016/j.ijepes.2015.11.052.Search in Google Scholar
2. Chow, JH, Sanchez-Gasca, JJ, Ren, H, Wang, S. Power system damping controller design-using multiple input signals. IEEE Contr Syst Mag 2000;20:82–90. https://doi.org/10.1109/37.856181.Search in Google Scholar
3. Mokhtari, M, Aminifar, F. Toward wide-area oscillation control through doubly-fed induction generator wind farms. IEEE Trans Power Syst 2014;29:2985–92. https://doi.org/10.1109/tpwrs.2014.2309012.Search in Google Scholar
4. Setiadi, H, Mithulananthan, N, Shah, R, Raghunathan, T, Jayabarathi, T. Enabling resilient wide-area POD at BESS in Java, Indonesia 500 kV power grid. IET Gener, Transm Distrib 2019;13:3734–44. https://doi.org/10.1049/iet-gtd.2018.6670.Search in Google Scholar
5. Movahedi, A, Niasar, AH, Gharehpetian, GB. Designing SSSC, TCSC, and STATCOM controllers using AVURPSO, GSA, and GA for transient stability improvement of a multi-machine power system with PV and wind farms. Int J Electr Power Energy Syst 2019;106:455–66. https://doi.org/10.1016/j.ijepes.2018.10.019.Search in Google Scholar
6. Mehta, B, Bhatt, P, Pandya, V. Small signal stability analysis of power systems with DFIG based wind power penetration. Int J Electr Power Energy Syst 2014;58:64–74. https://doi.org/10.1016/j.ijepes.2014.01.005.Search in Google Scholar
7. Yan, C, Yao, W, Wen, J, Fang, J, Ai, X, Wen, J. Optimal design of probabilistic robust damping controllers to suppress multiband oscillations of power systems integrated with wind farm. Renew Energy 2020;158:75–90. https://doi.org/10.1016/j.renene.2020.05.008.Search in Google Scholar
8. Chatterjee, S, Naithani, A, Mukherjee, V. Small-signal stability analysis of DFIG based wind power system using teaching learning based optimization. Int J Electr Power Energy Syst 2016;78:672–89. https://doi.org/10.1016/j.ijepes.2015.11.113.Search in Google Scholar
9. Surinkaew, T, Ngamroo, I. Coordinated robust control of DFIG wind turbine and PSS for stabilization of power oscillations considering system uncertainties. IEEE Trans Sustain Energy 2014;5:823–33. https://doi.org/10.1109/tste.2014.2308358.Search in Google Scholar
10. Bakir, H, Kulaksiz, AA. Modelling and voltage control of the solar-wind hybrid micro-grid with optimized STATCOM using GA and BFA. Eng Sci Technol Int J 2020;23:576–84. https://doi.org/10.1016/j.jestch.2019.07.009.Search in Google Scholar
11. Song, M, Chen, K, Wang, J. A two-level approach for three-dimensional micro-siting optimization of large-scale wind farms. Energy 2020;190:116340. https://doi.org/10.1016/j.energy.2019.116340.Search in Google Scholar
12. Navin, KP, Singh, AK, Singh, NK, Kumar, P. Optimal sizing and operation of battery storage for economic operation of hybrid power system using artificial bee colony algorithm. Int Trans Electr Energy Syst 2019;29:e2685.10.1002/etep.2685Search in Google Scholar
13. Hemeida, AM, Hassan, SA, Mohamed, Al-AA, Alkhalaf, S, Mahmoud, MM, Senjyu, T, et al.. Nature-inspired algorithms for feed-forward neural network classifiers: a survey of one decade of research. Ain Shams Eng J 2020;659–75. https://doi.org/10.1016/j.asej.2020.01.007.Search in Google Scholar
14. Ramesh, D, Bhattacharyya, B, Sinha, NK. An intelligent EGWO-SCA-CS algorithm for PSS parameter tuning under system uncertainties. Int J Intell Syst 2020;35:1520–69. https://doi.org/10.1002/int.v35.5.Search in Google Scholar
15. Mallaiah, M, Rama Rao, K, Venkateswarlu, C. A simulated annealing optimization algorithm based nonlinear model predictive control strategy with application. Evolving System 2020;12:1–7. https://doi.org/10.1007/s12530-020-09354-1.Search in Google Scholar
16. Guchhait, PK, Banerjee, A. Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm. Protect Contr Mod Power Syst 2020;5:1–13. https://doi.org/10.1186/s41601-020-00158-8.Search in Google Scholar
17. Barrios Aguilar, ME, Vinicius Coury, D, Reginatto, R, Machado Monaro, R. Multi-objective PSO applied to PI control of DFIG wind turbine under electrical fault conditions. Elec Power Syst Res 2020;180:106081. https://doi.org/10.1016/j.epsr.2019.106081.Search in Google Scholar
18. Yang, J-S, Chen, Y-W, Hsu, Y-Y. Small-signal stability analysis and particle swarm optimisation self-tuning frequency control for an islanding system with DFIG wind farm. IET Gener, Transm Distrib 2019;13:563–74. https://doi.org/10.1049/iet-gtd.2018.6101.Search in Google Scholar
19. Zhang, C, Ke, D, Sun, Y, Chung, CY, Xu, J, Shen, F. Coordinated supplementary damping control of DFIG and PSS to suppress inter-area oscillations with optimally controlled plant dynamics. IEEE Trans Sustain Energy 2017;9:780–91.10.1109/PESGM.2018.8586438Search in Google Scholar
20. Li, P, Xiong, L, Wu, F, Ma, M, Wang, J. Sliding mode controller based on feedback linearization for damping of sub-synchronous control interaction in DFIG-based wind power plants. Int J Electr Power Energy Syst 2019;107:239–50. https://doi.org/10.1016/j.ijepes.2018.11.020.Search in Google Scholar
21. Mir, M, Dayyani, M, Sutikno, T, Mohammadi Zanjireh, M, Razmjooy, N. Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output-fuzzy system as an integrated controller of a micro-grid with stability analysis. Comput Intell 2020;36:225–58. https://doi.org/10.1111/coin.12257.Search in Google Scholar
22. Mehta, B, Bhatt, P, Pandya, V. Small signal stability enhancement of DFIG based wind power system using optimized controllers parameters. Int J Electr Power Energy Syst 2015;70:70–82. https://doi.org/10.1016/j.ijepes.2015.01.039.Search in Google Scholar
23. Li, J, Huang, H, Chen, X, Peng, L, Wang, L, Luo, P. The small-signal stability of offshore wind power transmission inspired by particle swarm optimization. Complexity 2020;2020:1–13. https://doi.org/10.1155/2020/9438285.Search in Google Scholar
24. Khezri, R, Bevrani, H. Voltage performance enhancement of DFIG-based wind farms integrated in large-scale power systems: coordinated AVR and PSS. Int J Electr Power Energy Syst 2015;73:400–10. https://doi.org/10.1016/j.ijepes.2015.05.014.Search in Google Scholar
25. Ayres, H, Kopcak, I, Castro, M, Milano, F, Da Costa, V. A didactic procedure for designing power oscillation dampers of FACTS devices. Simulat Model Pract Theor 2010;18:896–909. https://doi.org/10.1016/j.simpat.2010.02.007.Search in Google Scholar
26. Amit, K, Bansal, RC. A supplementary controller for improvement of small signal stability of power system with wind power penetration. Elec Power Compon Syst 2016;44:1825–38.10.1080/15325008.2016.1183726Search in Google Scholar
27. Selvaraj, G, Rajangam, K. Multi-objective grey wolf optimizer algorithm for combination of network reconfiguration and D-STATCOM allocation in distribution system. Int Trans Electr Energy Syst 2019;29:e12100.10.1002/2050-7038.12100Search in Google Scholar
28. Yuan, C, Xu, Y. Multi-objective robust tuning of STATCOM controller parameters for stability enhancement of stochastic wind-penetrated power systems. IET Gener, Transm Distrib 2020;14:4805–14. https://doi.org/10.1049/gtd2.v14.22.Search in Google Scholar
29. Mohamed Faroug, MA, Narasimha Rao, D, Samikannu, R, Kumar Venkatachary, S, Senthilnathan, K. Comparative analysis of controllers for stability enhancement for wind energy system with STATCOM in the grid connected environment. Renew Energy 2020;162:2408–42. https://doi.org/10.1016/j.renene.2020.06.044.Search in Google Scholar
30. Ain, Q, Jamil, E, Hameed, S, Hasan Naqvi, K. Effects of SSSC and TCSC for enhancement of power system stability under different fault disturbances. Aust J Electr Electron Eng 2020;17:56–64. https://doi.org/10.1080/1448837x.2020.1752095.Search in Google Scholar
31. Movahedi, A, Halvaei Niasar, A, Gharehpetian, GB. LVRT improvement and transient stability enhancement of power systems based on renewable energy resources using the coordination of SSSC and PSSs controllers. IET Renew Power Gener 2019;13:1849–60. https://doi.org/10.1049/iet-rpg.2018.6010.Search in Google Scholar
32. Ackermann, T. Wind power in power systems. In: Wiley Online Library. Sweden: Royal Institute of Technology Stockholm; 2005. https://doi.org/10.1002/0470012684.10.1002/0470012684Search in Google Scholar
33. Qiao, W. Dynamic modeling and control of doubly fed induction generators driven by wind turbines. In: Power Systems Conference and Exposition, 2009. PSCE’09. IEEE/PES. Seattle: IEEE; 2009:1–8 pp. https://doi.org/10.1109/PSCE.2009.4840245.10.1109/PSCE.2009.4840245Search in Google Scholar
34. Sauer, PW, Pai, MA, Chow, JH. Power system dynamics and stability: with synchrophasor measurement and power system toolbox. Urbana: John Wiley & Sons; 2017.10.1002/9781119355755Search in Google Scholar
35. Mondal, D, Chakrabarti, A, Sengupta, A. Power system small signal stability analysis and control. Kolkata: Academic Press; 2014.10.1016/B978-0-12-800572-9.00004-4Search in Google Scholar
36. Stegink, TW, De Persis, C, Schaft, AJVD. An energy-based analysis of reduced-order models of (networked) synchronous machines. Math Comput Model Dyn Syst 2019;25:1–39. https://doi.org/10.1080/13873954.2019.1566265.Search in Google Scholar
37. Mohamed, E, Lo, KL, Anaya-Lara, O. Impacts of high penetration of DFIG wind turbines on rotor angle stability of power systems. IEEE Trans Sustain Energy 2015;6:759–66.10.1109/TSTE.2015.2412176Search in Google Scholar
38. Hingorani, NG, Gyugyi, L. Understanding FACTS: concepts and technology of flexible AC transmission systems. Piscataway: Wiley-IEEE press; 2000.Search in Google Scholar
39. Liu, S, Li, G, Zhou, M. Power system transient stability analysis with integration of DFIGs based on center of inertia. CSEE J Power Energy Syst 2016;2:20–9. https://doi.org/10.17775/cseejpes.2016.00018.Search in Google Scholar
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