Model-Based Receding Horizon Control of Wind Turbine System for Optimal Power Generation

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Wind power has many benefits over other energy sources, including a high power density and an outstanding return on investment. However, there are some drawbacks, such as intermittent output power and the need for periodic maintenance. As a result, its output is substantially variable, making it difficult to predict and potentially causing system instability. Therefore, to model such a source, it is necessary to model the dynamic behavior of the wind turbine generator as well as the characteristics of the wind speed to capture the fluctuations. Furthermore, the durability and efficiency of the wind energy conversion system (WECS) are wholly dependent on the quality of the control strategy employed. In this paper, we introduced a control scheme, which makes it possible to find an optimal solution to the control problem while at the same time operating within the constraint point. Therefore, we designed the Model Predictive Controller to control and smoothly transition the wind turbine in all its operating modes while complying with its constraints. The main objective of using this control technique is to maximize power production while keeping the control action as simple as possible. The WECS used in this study is the horizontal axis wind turbines (HAWT), which are easier to control as their dynamics are not so complicated to model and, at the same time, produce maximum output power. The controller works have to adapt in the same way as the control goals are different for different wind speeds. Gain and weight scheduling strategies are used to design a control system that allows smooth transitioning between control regions. The dynamics of the wind turbine system and the controller are designed and simulated by the MATLAB / Simulink environment.

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83-98

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April 2021

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© 2021 Trans Tech Publications Ltd. All Rights Reserved

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[1] L. Arturo Soriano, W. Yu, and J. d. J. Rubio, Modeling and control of wind turbine,, Mathematical Problems in Engineering, vol. 2013, (2013).

DOI: 10.1155/2013/982597

Google Scholar

[2] M. Beus and H. Pandžić, Application of Model Predictive Control Algorithm on a Hydro Turbine Governor Control,, in 2018 Power Systems Computation Conference (PSCC), pp.1-7, (2018).

DOI: 10.23919/pscc.2018.8442594

Google Scholar

[3] P. A. Gbadega and A. K. Saha, Model predictive controller design of a wavelength-based thermo-electrical model of a photovoltaic (PV) module for optimal output power,, in International Journal of Engineering Research in Africa, pp.133-148, (2020).

DOI: 10.4028/www.scientific.net/jera.48.133

Google Scholar

[4] P. A. Gbadega and A. K. Saha, Impact of Incorporating Disturbance Prediction on the Performance of Energy Management Systems in Micro-Grid,, IEEE Access, vol. 8, pp.162855-162879, (2020).

DOI: 10.1109/access.2020.3021598

Google Scholar

[5] M. S. Mahmoud, M. S. U. Rahman, and M.-S. Fouad, Review of microgrid architectures–a system of systems perspective,, IET Renewable Power Generation, vol. 9, pp.1064-1078, (2015).

DOI: 10.1049/iet-rpg.2014.0171

Google Scholar

[6] S. Sahoo, B. Subudhi, and G. Panda, Optimal speed control of DC motor using linear quadratic regulator and model predictive control,, in 2015 International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE), pp.1-5, (2015).

DOI: 10.1109/epetsg.2015.7510130

Google Scholar

[7] A. Alzahrani, M. Ferdowsi, P. Shamsi, and C. H. Dagli, Modeling and simulation of microgrid,, Procedia Computer Science, vol. 114, pp.392-400, (2017).

DOI: 10.1016/j.procs.2017.09.053

Google Scholar

[8] U. Maeder, F. Borrelli, and M. Morari, Linear offset-free model predictive control,, Automatica, vol. 45, pp.2214-2222, (2009).

DOI: 10.1016/j.automatica.2009.06.005

Google Scholar

[9] Henriksen, L. C., Poulsen, N. K., & Hansen, M. H. Model Predictive Control of Wind Turbines,. Kgs. Lyngby, Denmark: Technical University of Denmark (DTU). (IMM-PHD-2010-244), DTU Informatics, (2011).

DOI: 10.13052/jsame2245-4551.2018003

Google Scholar

[10] A. Alzahrani, P. Shamsi, M. Ferdowsi, and C. Dagli, Modeling and simulation of a microgrid using feedforward neural networks,, in 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA), pp.1001-1006, (2017).

DOI: 10.1109/icrera.2017.8191208

Google Scholar

[11] R. A. Waltz, J. L. Morales, J. Nocedal, and D. Orban, An interior algorithm for nonlinear optimization that combines line search and trust region steps,, Mathematical programming, vol. 107, pp.391-408, (2006).

DOI: 10.1007/s10107-004-0560-5

Google Scholar

[12] L. C. Henriksen and N. K. Poulsen, An online re-linearization scheme suited for Model Predictive and Linear Quadratic Control,, DTU Informatics, pp.1-14, (2010).

Google Scholar

[13] C. L. Bottasso, A. Croce, B. Savini, W. Sirchi, and L. Trainelli, Aero-servo-elastic modeling and control of wind turbines using finite-element multibody procedures,, Multibody System Dynamics, vol. 16, pp.291-308, (2006).

DOI: 10.1007/s11044-006-9027-1

Google Scholar

[14] L. C. Henriksen, M. H. Hansen, and N. K. Poulsen, Wind turbine control with constraint handling: a model predictive control approach,, IET control theory & applications, vol. 6, pp.1722-1734, (2012).

DOI: 10.1049/iet-cta.2011.0488

Google Scholar

[15] L. C. Henriksen, N. K. Poulsen, and M. H. Hansen, Nonlinear model predictive control of a simplified wind turbine,, IFAC Proceedings Volumes, vol. 44, pp.551-556, (2011).

DOI: 10.3182/20110828-6-it-1002.02070

Google Scholar

[16] S. Georg, H. Schulte, and H. Aschemann, Control-oriented modelling of wind turbines using a Takagi-Sugeno model structure,, in 2012 IEEE International Conference on Fuzzy Systems, pp.1-8, (2012).

DOI: 10.1109/fuzz-ieee.2012.6251302

Google Scholar

[17] A. Gosk, Model predictive control of a wind turbine," Master,s thesis, Technical University of Denmark, (2011).

Google Scholar

[18] S. Nimmagadda, A. Islam, S. B. Bayne, J. Sanchez, and L. Garcia Caballero, Improvements in the modeling of wind turbines in power system studies,, Journal of Renewable and Sustainable Energy, vol. 7, p.043147, (2015).

DOI: 10.1063/1.4928678

Google Scholar

[19] P.A. Gbadega and A.K Saha, Effects and Performance Indicators Evaluation of PV Array Topologies on PV Systems Operation Under Partial Shading Conditions,, in 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA), pp.322-327, (2019).

DOI: 10.1109/robomech.2019.8704823

Google Scholar

[20] H. Wen and R. Yang, Modeling and simulation of energy control strategies in AC Microgrid,, in 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp.1469-1474, (2016).

DOI: 10.1109/appeec.2016.7779734

Google Scholar

[21] E. Hamatwi, I. E. Davidson, M. Gitau, and G. Adam, Modeling and control of voltage source converters for grid integration of a wind turbine system,, in 2016 IEEE PES PowerAfrica, pp.98-106, (2016).

DOI: 10.1109/powerafrica.2016.7556579

Google Scholar

[22] A. J. Larsen and T. S. Mogensen, Individuel pitchregulering af vindmølle,, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark, (2006).

DOI: 10.13052/jsame2245-4551.2018003

Google Scholar

[23] E. Bossanyi, T. Burton, D. Sharpe, and N. Jenkins, Wind energy handbook,, ed: New York: Wiley, (2000).

Google Scholar

[24] P.A. Gbadega and A. Saha, Electrical characteristics improvement of photovoltaic modules using a two-diode model and its application under mismatch conditions,, in 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA), pp.328-333, (2019).

DOI: 10.1109/robomech.2019.8704846

Google Scholar

[25] P. A. Gbadega and A. K. Saha, Load frequency control of a two-area power system with a stand-alone micro-grid based on adaptive model predictive control,, IEEE Journal of Emerging and Selected Topics in Power Electronics, (2020).

DOI: 10.1109/jestpe.2020.3012659

Google Scholar

[26] P.A. Gbadega and A.K Saha, Adaptive model-based receding horizon control of interconnected renewable-based power micro-grids for effective control and optimal power exchanges,, in 2020 International SAUPEC/RobMech/PRASA Conference, pp.1-6, (2020).

DOI: 10.1109/saupec/robmech/prasa48453.2020.9041136

Google Scholar

[27] P. A. Gbadega and A. K. Saha, Dynamic Tuning of the Controller Parameters in a Two-Area Multi-Source Power System for Optimal Load Frequency Control Performance,, in International Journal of Engineering Research in Africa, pp.111-129, (2020).

DOI: 10.4028/www.scientific.net/jera.51.111

Google Scholar

[28] G. Pannocchia and J. B. Rawlings, Disturbance models for offset‐free model‐predictive control,, AIChE journal, vol. 49, pp.426-437, (2003).

DOI: 10.1002/aic.690490213

Google Scholar

[29] P. A. Gbadega and K. T. Akindeji, Linear Quadratic Regulator Technique for Optimal Load Frequency Controller Design of Interconnected Linear Power Systems,, in 2020 IEEE PES/IAS PowerAfrica, pp.1-5, (2020).

DOI: 10.1109/powerafrica49420.2020.9219887

Google Scholar

[30] J. Jonkman, S. Butterfield, W. Musial, and G. Scott, Definition of a 5-MW reference wind turbine for offshore system development,, National Renewable Energy Lab.(NREL), Golden, CO (United States), (2009).

DOI: 10.2172/947422

Google Scholar