Model predictive control stabilization of a power system including a wind power plant

Year 2022, Volume: 6 Issue: 2, 188 - 208, 30.06.2022

Islam Ahmed Ali , Abdel Latif Elshafei

https://doi.org/10.30521/jes.997307

Cited By: 3

Abstract

The conventional generators are equipped with power system stabilizers (PSS) to damp oscillations that follow disturbances. The inclusion of renewable energy sources within the existing power systems requires further investigations to enhance the performance of PSS. Several control strategies have been being used to design the PSS. In this paper, model predictive control (MPC) is investigated to be used as a PSS. It uses numerical optimization algorithms to get an optimal control output considering the system’s constraints. Therefore, It is designed and applied to a multi-machine power system with a wind power plant (WPP). Three disturbances are used to test the controllers including three-phase fault, transmission line outage, and voltage reference sudden change. MATLAB/SIMULINK is used in the simulation. Then, the results are compared to conventional multi-band controller (MB) and linear quadratic regulator (LQR). MPC shows efficient performance in handling the constraints and damping types of oscillations with the existence of the WPP in the case of partial power-sharing.

Keywords

Linear quadratic regulator, Model predictive control, Power system stabilizer, State feedback control, Wind power plant

Supporting Institution

Cairo University, Faculty of Engineering, Electrical Power Engineering Department

Project Number

M.Sc. Thesis

References

• [1] Renewable Capacity Statistics 2020. International Renewable Energy Agency (IRENA), 2020.
• [2] IEEE Recommended Practice for Excitation System Models for Power System Stability Studies. IEEE Std 421.5-2005 (Revision of IEEE Std 421.5-1992) 2006; 10 Aug. 1992: IEEE, pp. 1-93.
• [3] IEEE Recommended Practice for Excitation System Models for Power System Stability Studies. IEEE Std 421.5-2016 (Revision of IEEE Std 421.5-2005) 2016; 15 May 2016: IEEE: pp. 1-207.
• [4] Chen, S, Malik, OP, Chen, T. A Robust Power System Stabilizer Design. Optimal Control Applications and Methods 1997; 18(3): 179-193. DOI:10.1002/(SICI)1099-1514(199705/06)18:3<179::AID-OCA597>3.0.CO;2-5
• [5] Abdelazim, T, Malik, OP. Power System Stabilizer Based on Model Reference Adaptive Fuzzy Control. Electrical Power Components and Systems 2005; 33(9): 985-998. DOI:10.1080/15325000590921017
• [6] Harmas, N, Essounbouli, H. A New Robust Adaptive Fuzzy Sliding Mode Power System Stabilizer. Electrical Power and Energy Systems 2012; 42: 1-7. DOI:10.1016/j.ijepes.2012.03.032
• [7] Yee, SK, Milanović, JV. Fuzzy logic controller for decentralized stabilization of multimachine power systems. IEEE Transactions on Fuzzy Systems 2008; 16(4): 971-981. DOI:10.1109/TFUZZ.2008.917296
• [8] Changaroon, B., Srivastava, SC, Thukaram, D. A neural network-based power system stabilizer suitable for an online training-a practical case study for EGAT system. IEEE Transactions on Energy Conversion 2000; 15(1): 103-109. DOI:10.1109/60.849124
• [9] Chaturvedi, DK, Malik OP. Neuro-fuzzy Power System Stabilizer. IEEE Transactions on Energy Conversion 2008; 23(3): 887-894. DOI:10.1109/TEC.2008.918633
• [10] Soliman, M, Elshafei, AL, Bendary, F, Mansour, W. Robust Decentralized PID-based Power System Stabilizer Design Using an ILMI Approach. Electric Power System Research 2010; 80(12): 1488-1497. DOI:10.1016/j.epsr.2010.06.008
• [11] Ibrahim, H, Ghandour, M, Dimitrova, M, Ilinca, A, Perron, J. Integration of Wind Energy into Electricity Systems. Technical Challenges and Actual Solutions. Energy Procedia 2011: 6: 815-824. DOI:10.1016/j.egypro.2011.05.092
• [12] Kalogiannis, T, Llano, EM, Hoseinzadeh, B, da Silva, FF. Impact of high level penetration of wind turbines on power system transient stability: IEEE Eindhoven PowerTech 2015; 7232312: 1-6. DOI:10.1109/PTC.2015.7232312
• [13] Yang, J, Sun, X, Liao, K, He, Z, Cai, L. Model predictive control-based load frequency control for power systems with wind-turbine generators. IET Renewable Power Generation 2019; 13(15): 2871–2879. DOI: 10.1049/iet-rpg.2018.6179
• [14] Dang, DQ, Wu, S, Wang, Y, Cai, W. Model Predictive Control for maximum power capture of variable speed wind turbines. In: IPEC 2010 International Power Engineering Conference; 27-29 Oct. 2010: IEEE, pp.274-279. DOI:10.1109/IPECON.2010.5697119
• [15] Zhao, H, Wu, Q, Guo, Q, Sun, H, Huang, S, Xue, Y. Coordinated Voltage Control of a Wind Farm Based on Model Predictive Control. IEEE Transactions on Sustainable Energy 2016; 7(4): 1440-1451. DOI:10.1109/TSTE.2016.2555398
• [16] Kundur, P. Power System Stability, and Control. New York, USA: McGraw-Hill, Inc, 1994.
• [17] Klein, M, Rogers, GJ, Moorty, S, Kundur, P. Analytical investigation of factors influencing power system stabilizers performance. IEEE Transactions on Energy Conversion 1992; 7(3): 382-390. DOI: 10.1109/60.148556.
• [18] Ashraf, M, Elshafei, AL, Eldeeb, A. Wind Power Plant Modeling and Control Design for Inter-Area Oscillation Damping. MSc. Cairo University, Giza, Egypt, 2019.
• [19] Overschee, PV, Moor, BD. N4SID Subspace Algorithms for the Identification of Combined Deterministic-Stochastic Systems. Automatica 1994; 30: 75-93. DOI:10.1016/0005-1098(94)90230-5
• [20] Ljung, L. System Identification Toolbox User's Guide, MATLAB. Mathworks, 2012.
• [21] Kouvaritakis, B, Cannon, M. Model Predictive Control: Classical, Robust and Stochastic. Oxford, UK. Springer, 2016.
• [22] Ogata, K. Discrete-Time Control Systems.New Jersey, USA: Prentice-Hall, 1995.