Adaptive super-twisting sliding mode control for maximum power point tracking of PMSG-based wind energy conversion systems

被引:59
作者
Zholtayev, Darkhan [1 ]
Rubagotti, Matteo [1 ]
Do, Ton Duc [1 ]
机构
[1] Nazarbayev Univ, Sch Engn & Digital Sci, Dept Robot & Mechatron, Kabanbay Batyr Ave 53, Nur Sultan City 010000, Kazakhstan
关键词
Sliding mode control; Permanent magnet synchronous generators; Wind energy conversion systems; OF-THE-ART; CONTROL DESIGN; TURBINE; SPEED; ALGORITHM; GAINS;
D O I
10.1016/j.renene.2021.11.055
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper proposes a super-twisting adaptive sliding mode control law for maximum power point tracking of wind energy conversion systems based on permanent magnet synchronous generators (PMSGs). The employed sliding mode control algorithm can be considered an effective solution, as it retains the robustness properties of classical sliding mode control methods in the presence of disturbances and parameter uncertainties; at the same time, it provides chattering reduction via gain adaptation and generation of second-order sliding modes. In our work, a nonlinear multi-input multi-output tracking control problem is solved to maximize the captured power. Simulation results are presented using real wind speed data, and discussed for the proposed controller and four other sliding mode control solutions for the same problem, in the presence of variations of stator resistance, stator inductance and magnetic flux linkage. The proposed controller achieves the best trade-off between tracking performance and chattering reduction among the five considered solutions: compared to a standard sliding mode control algorithm, it reduces the amount of chattering from two to five orders of magnitude, and the steady-state error on PMSG rotor velocity of one order of magnitude. Also, it reduces the above-mentioned steady-state error of four orders of magnitude with respect to a previously-proposed linear quadratic regulator based integral sliding mode control law for the same system.(c) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页码:877 / 889
页数:13
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