基于扰动观测器的永磁同步发电机最大功率跟踪滑模控制
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摘要
本文设计了一款基于扰动观测器的滑模控制(perturbation observer based sliding-mode control, POSMC)来实现永磁同步发电机(permanent magnetic synchronous generator, PMSG)的最大功率跟踪(maximum power point tracking, MPPT).首先,将发电机非线性、参数不确定、以及随机风速聚合成一个扰动,并通过扰动观测器对其进行在线估计.随后,采用滑模控制(sliding-mode control, SMC)对该扰动估计进行实时完全补偿,从而实现不同工况下的控制全局一致性以及各类不确定环境下的鲁棒控制.同时, POSMC采用扰动实时估计值进行补偿来代替传统SMC中所使用的扰动上限值进行补偿,因此可有效解决传统SMC过于保守的缺点,使得控制成本更为合理.最后, POSMC无需精确的PMSG模型,仅需测量d轴电流和机械转速,易于实现.本文进行了3个算例研究,即阶跃风速、随机风速和发电机参数不确定.仿真结果表明,与矢量控制(vector control, VC)和SMC相比, POSMC在各类工况下均可捕获最大风能并具有较强的鲁棒性.基于d Space的硬件在环实验(hardware-in-loop, HIL)验证了所提算法的可行性.
This paper designs a perturbation observer based sliding-mode control(POSMC) of permanent magnetic synchronous generator(PMSG) for maximum power point tracking(MPPT). Firstly, the generator nonlinearities, parameter uncertainties, unmodelled dynamics, stochastic wind speed are all aggregated into a perturbation, which is estimated online by a perturbation observer. Then, sliding-mode control(SMC) is employed to fully compensate the perturbation estimate,such that a globally consistent control performance under various operation conditions and significant robustness against different uncertainties can be achieved. Meanwhile, POSMC adopts the perturbation estimate to replace the upper bound of perturbation used in SMC, thus POSMC can effectively resolve the over-conservativeness of SMC hence more proper control costs could be resulted in. Lastly, POSMC is easy to be implemented as it does not require an accurate PMSG model while only the measurement of d-axis current and mechanical rotation speed is needed. Three cases are carried out, including step change of wind speed, stochastic wind speed, and generator parameter uncertainties. Simulation results demonstrate that POSMC can extract the optimal wind power and provide the greatest robustness in various operation conditions compared to that of vector control(VC) and SMC. At last, dSpace based hardware-in-loop(HIL) test validates the implementation feasibility of the proposed approach.
This paper designs a perturbation observer based sliding-mode control(POSMC) of permanent magnetic synchronous generator(PMSG) for maximum power point tracking(MPPT). Firstly, the generator nonlinearities, parameter uncertainties, unmodelled dynamics, stochastic wind speed are all aggregated into a perturbation, which is estimated online by a perturbation observer. Then, sliding-mode control(SMC) is employed to fully compensate the perturbation estimate,such that a globally consistent control performance under various operation conditions and significant robustness against different uncertainties can be achieved. Meanwhile, POSMC adopts the perturbation estimate to replace the upper bound of perturbation used in SMC, thus POSMC can effectively resolve the over-conservativeness of SMC hence more proper control costs could be resulted in. Lastly, POSMC is easy to be implemented as it does not require an accurate PMSG model while only the measurement of d-axis current and mechanical rotation speed is needed. Three cases are carried out, including step change of wind speed, stochastic wind speed, and generator parameter uncertainties. Simulation results demonstrate that POSMC can extract the optimal wind power and provide the greatest robustness in various operation conditions compared to that of vector control(VC) and SMC. At last, dSpace based hardware-in-loop(HIL) test validates the implementation feasibility of the proposed approach.
引文
[1] SYED I M, RAAHEMIFAR K. Predictive energy management, control and communication system for grid tied wind energy conversion systems. Electric Power Systems Research, 2017, 142:298–309.
[2] YANG B, ZHANG X S, YU T, et al. Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine. Energy Conversion and Management, 2017,133:427–443.
[3] HUANG Shoudao, LU Jining, HUANG Keyuan, et al. Optimized hill-climb search algorithm applied to directly driven wind-turbine with permanent-magnet synchronous generator. Control Theory&Applications, 2010, 27(9):1221–1226.(黄守道,卢季宁,黄科元,等.优化爬山算法在直驱永磁风力发电系统中的应用.控制理论与应用, 2010, 27(9):1221–1226.)
[4] AI Chao, CHEN Lijuan, KONG Xiangdong, et al. Application of feedback linearization method in power point tracking of hydraulic wind turbine. Control Theory&Applications, 2016, 33(7):915–922.(艾超,陈立娟,孔祥东,等.反馈线性化在液压型风力发电机组功率追踪中的应用.控制理论与应用, 2016, 33(7):915–922.)
[5] SHEHATA E G. A comparative study of current control schemes for a direct-driven PMSG wind energy generation system. Electric Power Systems Research, 2017, 143:197–205.
[6] CHEN J, JIANG L, YAO W, et al. A feedback linearization control strategy for maximum power point tracking of a PMSG based wind turbine. International Conference on Renewable Energy Research and Applications, Madrid. Spain:IEEE, 2013:79–84.
[7] ZU Hui, ZHANG Guobao, FEI Shumin, et al. Robust H-infinity control for permanent-magnet synchronous generators in wind-energy conversion system based on uncertain descriptor model. Control Theory&Applications, 2011, 28(11):1634–1640.(祖晖,章国宝,费树岷,等.基于不确定广义模型的永磁同步风力发电机鲁棒H∞控制.控制理论与应用, 2011, 28(11):1634–1640.)
[8] FANTINO R, SOLSONA J, BUSADA C. Nonlinear observer-based control for PMSG wind turbine. Energy, 2016, 113:248–257.
[9] WEI C, ZHANG Z, QIAO W, QU L Y. An adaptive network-based reinforcement learning method for MPPT control of PMSG wind energy conversion systems. IEEE Transactions on Power Electronics,2016, 31(11):7837–7848.
[10] LI S Q, ZHANG K Z, LI J, et al. On the rejection of internal and external disturbances in a wind energy conversion system with directdriven PMSG. ISA Transactions, 2016, 61:95–103.
[11] YANG B, HU Y L, HUANG H Y, et al. Perturbation estimation based robust state feedback control for grid connected DFIG wind energy conversion system. International Journal of Hydrogen Energy, 2017,42(33):20994–21005.
[12] YANG B, YU T, SHU H C, et al. Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers. Applied Energy, 2018, 210:711–723.
[13] HU Cungang, DENG Na, ZHANG Yunlei, et al. Optimal control strategy of neutral-point potential for three-level active neutral-pointclamped microgrid energy storage converter. Control Theory&Applications, 2017, 34(8):1092–1103.(胡存刚,邓娜,张云雷,等.三电平有源中点钳位型微网储能变流器直流侧中点电压优化控制策略.控制理论与应用, 2017, 34(8):1092–1103.)
[14] FERNANDO V, ROBERTO D F. Multiple-input-multiple-output high-order sliding mode control for a permanent magnet synchronous generator wind-based system with grid support capabilities. IET Renewable Power Generation, 2015, 9(8):925–934.
[15] HUERTA F, TELLO R L, PRODANOVIC M. Real-time powerhardware-in-the-loop implementation of variable-speed wind turbines. IEEE Transactions on Industrial Electronics, 2017, 64(3):1893–1904.
[16] YANG B, YU T, SHU H C, et al. Passivity-based sliding-mode control design for optimal power extraction of a PMSG based variable speed wind turbine. Renewable Energy, 2018, 119:577–589.
[17] ZHANG Yueling. Sliding mode control based on adaptive hysteresis function for electromechanical actuator. Micromotors, 2016, 49(9):71–75.(张月玲.基于滞环函数的电动舵机滑模控制.微电机, 2016, 49(9):71–75.)
[18] ORTEGA R, SCHAFT A, MAREELS I, et al. Putting energy back in control. IEEE Control Systems, 2001, 21(2):18–33.
[19] KHALIL H K. Nonlinear Systems. Second edition. London:PrenticeHall Inc, 1996.
[2] YANG B, ZHANG X S, YU T, et al. Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine. Energy Conversion and Management, 2017,133:427–443.
[3] HUANG Shoudao, LU Jining, HUANG Keyuan, et al. Optimized hill-climb search algorithm applied to directly driven wind-turbine with permanent-magnet synchronous generator. Control Theory&Applications, 2010, 27(9):1221–1226.(黄守道,卢季宁,黄科元,等.优化爬山算法在直驱永磁风力发电系统中的应用.控制理论与应用, 2010, 27(9):1221–1226.)
[4] AI Chao, CHEN Lijuan, KONG Xiangdong, et al. Application of feedback linearization method in power point tracking of hydraulic wind turbine. Control Theory&Applications, 2016, 33(7):915–922.(艾超,陈立娟,孔祥东,等.反馈线性化在液压型风力发电机组功率追踪中的应用.控制理论与应用, 2016, 33(7):915–922.)
[5] SHEHATA E G. A comparative study of current control schemes for a direct-driven PMSG wind energy generation system. Electric Power Systems Research, 2017, 143:197–205.
[6] CHEN J, JIANG L, YAO W, et al. A feedback linearization control strategy for maximum power point tracking of a PMSG based wind turbine. International Conference on Renewable Energy Research and Applications, Madrid. Spain:IEEE, 2013:79–84.
[7] ZU Hui, ZHANG Guobao, FEI Shumin, et al. Robust H-infinity control for permanent-magnet synchronous generators in wind-energy conversion system based on uncertain descriptor model. Control Theory&Applications, 2011, 28(11):1634–1640.(祖晖,章国宝,费树岷,等.基于不确定广义模型的永磁同步风力发电机鲁棒H∞控制.控制理论与应用, 2011, 28(11):1634–1640.)
[8] FANTINO R, SOLSONA J, BUSADA C. Nonlinear observer-based control for PMSG wind turbine. Energy, 2016, 113:248–257.
[9] WEI C, ZHANG Z, QIAO W, QU L Y. An adaptive network-based reinforcement learning method for MPPT control of PMSG wind energy conversion systems. IEEE Transactions on Power Electronics,2016, 31(11):7837–7848.
[10] LI S Q, ZHANG K Z, LI J, et al. On the rejection of internal and external disturbances in a wind energy conversion system with directdriven PMSG. ISA Transactions, 2016, 61:95–103.
[11] YANG B, HU Y L, HUANG H Y, et al. Perturbation estimation based robust state feedback control for grid connected DFIG wind energy conversion system. International Journal of Hydrogen Energy, 2017,42(33):20994–21005.
[12] YANG B, YU T, SHU H C, et al. Robust sliding-mode control of wind energy conversion systems for optimal power extraction via nonlinear perturbation observers. Applied Energy, 2018, 210:711–723.
[13] HU Cungang, DENG Na, ZHANG Yunlei, et al. Optimal control strategy of neutral-point potential for three-level active neutral-pointclamped microgrid energy storage converter. Control Theory&Applications, 2017, 34(8):1092–1103.(胡存刚,邓娜,张云雷,等.三电平有源中点钳位型微网储能变流器直流侧中点电压优化控制策略.控制理论与应用, 2017, 34(8):1092–1103.)
[14] FERNANDO V, ROBERTO D F. Multiple-input-multiple-output high-order sliding mode control for a permanent magnet synchronous generator wind-based system with grid support capabilities. IET Renewable Power Generation, 2015, 9(8):925–934.
[15] HUERTA F, TELLO R L, PRODANOVIC M. Real-time powerhardware-in-the-loop implementation of variable-speed wind turbines. IEEE Transactions on Industrial Electronics, 2017, 64(3):1893–1904.
[16] YANG B, YU T, SHU H C, et al. Passivity-based sliding-mode control design for optimal power extraction of a PMSG based variable speed wind turbine. Renewable Energy, 2018, 119:577–589.
[17] ZHANG Yueling. Sliding mode control based on adaptive hysteresis function for electromechanical actuator. Micromotors, 2016, 49(9):71–75.(张月玲.基于滞环函数的电动舵机滑模控制.微电机, 2016, 49(9):71–75.)
[18] ORTEGA R, SCHAFT A, MAREELS I, et al. Putting energy back in control. IEEE Control Systems, 2001, 21(2):18–33.
[19] KHALIL H K. Nonlinear Systems. Second edition. London:PrenticeHall Inc, 1996.