AUGMENTED PREDICTIVE INTELLIGENT CONTROL OF WIND TURBINE LOAD BASED ON ADAPTIVE NONSINGULAR TERMINAL SLIDING MODE OBSERVER

Ma Leiming; Xiao Lingfei; Jiang Bin

doi:10.19912/j.0254-0096.tynxb.2021-0477

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (11) : 259-268. DOI: 10.19912/j.0254-0096.tynxb.2021-0477

AUGMENTED PREDICTIVE INTELLIGENT CONTROL OF WIND TURBINE LOAD BASED ON ADAPTIVE NONSINGULAR TERMINAL SLIDING MODE OBSERVER

Ma Leiming¹, Xiao Lingfei², Jiang Bin¹

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Abstract

An augmented predictive intelligent control strategy of wind turbine load based on adaptive nonsingular terminal sliding mode observer（ANTSMO） is proposed to effectively reduce the unbalanced load. Firstly, aiming at the performance degradation of model predictive control（MPC） caused by model mismatch, the command tracking error and the change of state are augmented to state vector, and the augmented MPC is designed to eliminate the steady-state tracking error. Secondly, an ANTSMO is designed to estimate the state parameters, which can improve the reliability of the control system. Then, the multi-objective variable speed grey wolf optimization（MOVGWO） algorithm is designed to optimize the controller parameters. Finally, the effectiveness of the proposed control strategy is verified based on Simulink platform. The results show that the proposed control strategy can effectively eliminate the steady-state error, shorten the regulation time and improve the control performance.

Key words

wind turbines / load control / model predictive control / sliding mode control / multiobjective optimization

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Ma Leiming, Xiao Lingfei, Jiang Bin. AUGMENTED PREDICTIVE INTELLIGENT CONTROL OF WIND TURBINE LOAD BASED ON ADAPTIVE NONSINGULAR TERMINAL SLIDING MODE OBSERVER[J]. Acta Energiae Solaris Sinica. 2022, 43(11): 259-268 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0477

References

[1] 孔屹刚, 王杰, 顾浩, 等. 大型风力机气动载荷分析与功率控制[J]. 太阳能学报, 2012, 33(6): 1023-1029.
KONG Y G, WANG J, GU H, et al.Aerodynamic load analysis and power control for large wind turbine[J]. Acta energiae solaris sinica, 2012, 33(6): 1023-1029.
[2] BOSSANYI E A.Further load reductions with individual pitch control[J]. Wind energy, 2005, 8(4): 481-485.
[3] NOVAES M E J, ARAUJO A M, ROHATGI J S, et al. Active load control of large wind turbines using state-space methods and disturbance accommodating control[J]. Energy, 2018, 150: 310-319.
[4] GUI K, CEN L H, LIU F.Complementary sliding mode control for variable speed variable pitch wind turbine based on feedback linearization[C]//2020 Chinese Control and Decision Conference(CCDC), Hefei, China, 2020.
[5] LIN Z, CHEN Z, WU Q, et al.Coordinated pitch & torque control of large-scale wind turbine based on Pareto efficiency analysis[J]. Energy, 2018, 147: 812-825.
[6] MOHAMMADI E, FADAEINEDJAD R, MOSCHOPOULOS G.Implementation of internal model based control and individual pitch control to reduce fatigue loads and tower vibrations in wind turbines[J]. Journal of sound and vibration, 2018, 421: 132-152.
[7] PETROVI V, JELAVI M, BAOTI M.MPC framework for constrained wind turbine individual pitch control[J]. Wind energy, 2021, 24: 1-15.
[8] YUAN Y, CHEN X, TANG J.Multivariable robust blade pitch control design to reject periodic loads on wind turbines[J]. Renewable energy, 2020, 146: 329-341.
[9] SARKER R, ABBASS H A.Differential evolution for solving multi-objective optimization problems[J]. Asia-Pacific journal of operational research, 2004, 21(2): 225-240.
[10] DEB K.Multi-objective genetic algorithms: Problem difficulties and construction of test problems[J]. Evolutionary computation, 1999, 7(3): 205-230.
[11] COELLO C A C, PULIDO G T, LECHUGA M S. Handling multiple objectives with particle swarm optimization[J]. IEEE transactions on evolutionary computation, 2004, 8(3): 256-279.
[12] MIRJALILI S, SAREMI S, MIRJALILI S M, et al.Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization[J]. Expert systems with applications, 2015, 47: 106-119.
[13] 林歆悠, 王召瑞. 应用粒子群算法优化模糊规则的自适应多目标控制策略[J]. 控制理论与应用, 2021, 38(6): 842-850.
LIN X Y, WANG Z R.Adaptive multi-objective control strategy based on particle swarm optimization algorithm optimized fuzzy rules[J]. Control theory & applications, 2021, 38(6): 842-850.
[14] 王凯迪, 李迪, 冷杨松, 等. 基于径向基函数神经网络模型的车门结构多目标优化[J]. 山东理工大学学报(自然科学版), 2021, 35(2): 77-82.
WANG K D, LI D, LENG Y S, et al.Multi-objective optimization of door structure based on RBF neural network model[J]. Journal of Shandong University of Technology (natural science edition), 2021, 35(2): 77-82.
[15] 李星辰, 袁旭峰, 李沛然, 等. 基于改进QPSO算法的微电网多目标优化运行策略[J]. 电力科学与工程, 2020, 36(12): 22-29.
LI X C, YUAN X F, LI P R, et al.Multi-objective optimal operation strategy of microgrid based on improved QPSO algorithm[J]. Electric power science and engineering, 2020, 36(12): 22-29.
[16] MIRJALILI S, MIRJALILI S M, LEWIS A.Grey wolf optimizer[J]. Advances in engineering software, 2014, 69: 46-61.
[17] 邓英. 风力发电机组设计与技术[M]. 北京: 化学工业出版社, 2011.
DENG Y.Design and technology of wind turbine[M]. Beijing: Chemical Industry Press, 2011.
[18] 叶杭冶. 风力发电机组的控制技术[M]. 北京: 机械工业出版社, 2015.
YE H Y.Control technology of wind turbine[M]. Beijing: China Machine Press, 2015.