Blade Pitch Control Strategy for Floating Wind Turbines Based on Fractional Order PIDD²

Abstract

The current study focuses on mitigating the effects of wind and wave disturbances on the power output, rotor speed, and platform dynamics of floating wind turbines, while tackling the strong coupling and dynamic interference present in offshore environments. This study utilizes an OpenFAST-Simulink co-simulation framework to analyze the dynamic characteristics of the NREL 5 MW semi-submersible wind turbine system. A fractional-order PIDD² (FOPIDD²) controller is developed on top of the OpenFAST baseline framework for collective blade pitch regulation. The developed collective pitch controller can effectively suppress the external environmental disturbances, improving the robustness of the system against disturbances. Additionally, the parameters of the FOPIDD² controller are fine-tuned using the Ant–Lion optimization method to improve system regulation. Finally, the proposed FOPIDD² controller is systematically evaluated and compared with the FOPID, PIDD², and OpenFAST baseline controllers under different wind and wave scenarios. Simulation results indicate that the FOPIDD² controller enhances both the smoothness of power output and the accuracy of rotor speed regulation, thereby effectively suppressing the oscillatory movements (surge, pitch, and roll) of the platform, and improving the overall dynamic stability and robustness of the wind turbine system.

Keywords

Floating turbines / Pitch control / FOPIDD² / Wind-wave coupling / Ant–lion optimization

References

[1] Ali S, Yang G, Huang C. Performance optimization of linear active disturbance rejection control approach by modified bat inspired algorithm for single area load frequency control concerning high wind power penetration. ISA Transactions, 2018, 81: 163-176

[2] Asgharnia A, Shahnaz R, Jamali A. Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms. ISA Transactions, 2018, 79: 27-44

[3] Asgharnia A, Jamali A, Shahnazi R, Maheri A. Load mitigation of a class of 5-MW wind turbine with RBF neural network based fractional-order PID controller. ISA Transctations, 2020, 96: 272-286

[4] Avellaneda-Gomez LS, Grisales-Noreña LF, Cortés-Caicedo B, Montoya OD, Bolaños RI. Optimal battery operation for the optimization of power distribution networks: An application of the ant lion optimizer. Journal of Energy Storage, 2024, 84: 110684 Part A)

[5] Bianchi FD, Mantz RJ, Battista H. Wind turbine control systems: principles, modelling and gain scheduling design, 2007, London, Springer

[6] Chen H, Niu J, Cai Y, Aït-Ahmed N, Aït-Ahmed M, Benbouzid M. Adaptive sliding mode-based feedback linearization control for floating offshore wind turbine in region II. International Journal of Green Energy, 2025, 22(2): 467-486

[7] Civelek Z, Çam E, Lüy M, Mamur H. Proportional–integral – derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm. IET Renewable Power Generation, 2016, 10(81220-1228

[8] Cross P, Ma X. Nonlinear system identification for modelbased condition monitoring of wind turbines. Renewable Energy, 2014, 71: 166-175

[9] Goyal S, Deolia VK, Agrawal S. Performance evaluation of GA-optimized TSFL pitch controller for 2 mass drive train HAWTs. Journal of Electrical Engineering & Technology, 2024, 19: 3515-3526

[10] Hu X, Tan W, Hou G. PIDD² control of large wind turbines’ pitch angle. Energies, 2023, 16(13): 5096

[11] Huang B, Zhou L, Zhao H, Long L. Pitch control strategy for wind turbine considering operation efficiency. International Joint Conference on Energy, Electrical and Power Engineering (CoEEPE), Singapore: Springer, 2022, 1060: 1033-1045

[12] Huang C, Zhao H. Error-based active disturbance rejection control for wind turbine output power regulation. IEEE Transactions on Sustainable Energy, 2023, 14(31692-1701

[13] International Electrotechnical Commission. IEC 61400-3:2009 Wind turbines part3: Design requirements for offshore wind turbines, 2009, Geneva, International Electrotechnical Commission

[14] Izadi S, Ahmadi M, Nikbazm R. Network traffic classification using convolutional neural network and ant-lion optimization. Computers and Electrical Engineering, 2022, 101: 108024

[15] Izci D, Abualigah L, Can Ö, Andiç C, Ekinci S. Achieving improved stability for automatic voltage regulation with fractional-order PID plus double-derivative controller and mountain gazelle optimizer. International Journal of Dynamics and Control, 2024, 12: 2550-2565

[16] Jonkman J, Butterfield S, Musial W, Scott G. Definition of a 5-MW reference wind turbine for offshore system development, 2009, Golden, National Renewable Energy Lab NREL/TP-500-38060

[17] Khokhar B, Dahiya S, Parmar KPS. A novel fractional order proportional integral derivative plus second-order derivative controller for load frequency control. International Journal of Sustainable Energy, 2021, 40(3): 235-252

[18] Kim MH, Lee SU. PID with a switching action controller for nonlinear systems of second-order controller canonical form. International Journal of Control, Automation and Systems, 2021, 19(7): 2343-2356

[19] Lan J, Chen N, Li H, Wang X. A review of fault diagnosis and prediction methods for wind turbine pitch systems. International Journal of Green Energy, 2024, 21(7): 1613-1640

[20] Mirjalili S. The ant lion optimizer. Advances in Engineering Software, 2015, 83: 80-98

[21] Psomas PM, Platis AN, Dagkinis IK, Dragovic B, Lilas TE, Nikitakos NV. Evaluating the dependability measures of a hybrid wind–wave power generation system under varied weather conditions. Journal of Marine Science and Application, 2025, 24: 753-773

[22] Serrano C, Sierra-Garcia JE, Santos M. Hybrid optimized fuzzy pitch controller of a floating wind turbine with fatigue analysis. Journal of Marine Science and Engineering, 2022, 10(11): 1769

[23] Sharma R, Pal A, Mittal N, Kumar L, Van S, Nam Y, Abouhawwash M. MOALG: A metaheuristic hybrid of multi-objective ant lion optimizer and genetic algorithm for solving design problems. Computers, Materials & Continua, 2024, 78(33489-3510

[24] Sule AH, Mokhtar AS, Bin Jamian JB, Sheikh UU, Khidrani A. Grey wolf optimizer tuned PI controller for enhancing output parameters of fixed speed wind turbine.. 2020 IEEE International Conference on Automatic Control and Intelligent Systems, 2020, Piscataway, IEEE118122

[25] Tabak A. Maiden application of fractional order PID plus second order derivative controller in automatic voltage regulator. International Transactions on Electrical Energy Systtems, 2021, 31(12): e13211

[26] Vu Hong Son P, Soulisa FV. A hybrid ant lion optimizer (ALO) algorithm for construction site layout optimization. Soft Computing in Civil Engineering, 2023, 7(450-71

[27] Xu Y, Jia L, Peng D, Yang W. Iterative Neuro-Fuzzy Hammerstein Model Based Model Predictive Control for Wind Turbines. IEEE Transactions on Industry Applications, 2023, 59(5): 6501-6512

[28] Yuan J, Cheng Z, Liu D. Design of variable pitch control method for floating wind turbine. Energies, 2023, 16(2): 821

[29] Zade BMZ, Mansouri N, Javidi MMM. A new hyperheuristic based on ant lion optimizer and tabu search algorithm for replica management in cloud environment. Artificial Intelligence Review, 2023, 56(99837-9947

[30] Zahra B, Salhi H, Mellit A. Wind turbine performance enhancement by control of pitch angle using PID controller and particle swarm optimization. 2017 5th International Conference on Electrical Engineering-Boumerdes, 2017, Piscataway, IEEE15

[31] Zhang Y, Yang X. Intelligent control for floating offshore wind turbines based on game theory with data-driven and reliability awareness. Energy Technology, 2023, 11(92300241

[32] Zhao Y, Yang X, Zhao Z. Pitch control for floating offshore wind turbines via model-dependent extended state observer-based active disturbance rejection control. Ocean Engineering, 2025, 316: 119883

[33] Zou Y, Bai X, Li L. Variable pitch fractional-order model-assisted active disturbance rejection controller for wind turbine active power regulation. Electric Power Systems Research, 2024, 233: 110424

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Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature

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