不确定性条件下风电场有功功率控制方法研究
|
摘要
目前,风电建设已经进入大规模风电场发展阶段,其在电网中所占的比例不断增大。大规模并网风电场有功功率输出的随机性和波动性,给电网有功功率的平衡带来挑战,严重影响电网的安全可靠运行。电网从安全运行的角度,要求风电场配置有功功率控制系统,具备有功功率输出控制能力。
风电场有功功率控制系统一般采用由机组本地控制层和场级集中控制层组成的分层控制结构,其机组级的有功功率控制、风电场级有功功率分配和有功功率输出控制,是使风电场有功功率输出满足电网调度要求的关键。随着电网对风电场有功功率输出和系统稳定运行要求的不断提高,近年来,发展和完善风电场有功功率控制系统中的控制方法和分配策略已经成为风力发电控制技术研究的重点。
风电场有功功率控制系统在运行中,受风电机组本身的非线性、强耦合性和不确定性以及风电场环境中风速干扰等因素的影响,这些严重限制了风电场有功功率输出控制能力。因此,本文对在上述不确定性条件下的风电场有功功率控制方法进行了深入的研究。主要研究成果和创新点如下：
1.针对有效风速测量不准确引起的机组最大功率跟踪误差问题,研究了一种基于高增益观测器的风电机组最大功率跟踪控制方法。首先设计高增益观测器在线估计气动力矩,并采用Newton-Raphson算法计算当前风速的估计值,从而获取最优转速。然后对引入高增益观测器的系统,设计基于反演控制方法的转速控制器,通过直接控制发电机电压实现最优转速和最大功率的跟踪。利用Lyapunov理论证明了该控制系统是一致终结有界,且观测误差和跟踪误差收敛到原点的一个小邻域内。仿真实验结果表明,该方法无需风速测量,使风电机组能够对其最大可发功率进行有效跟踪。
2.针对有功功率参考值跃变和风速干扰下的机组恒功率输出控制问题,研究了一种基于非线性跟踪微分器的有功功率跟踪控制方法。首先,通过非线性跟踪微分器平滑场级集中控制层设定的有功功率参考信号,减少其跃变对系统动静态特性和稳定性的影响。然后设计基于动态面的自适应转速控制器,采用自适应控制方法在线补偿风速干扰引起的系统参数不确定性,并根据Lyapunov稳定性理论推导出动态面控制的控制律和不确定参数的调节律。仿真实验结果表明,该方法使风电机组在风速干扰下能够有效地跟踪具有跃变特性的有功功率参考信号。
3.针对风电场有功功率分配中的机组最大可发功率估计问题,提出了一种基于改进灰色模型的超短期有功功率预测方法。首先,通过缓冲算子对风速异常数据进行弱化处理,利用预处理后的风速历史数据,建立超短期风速灰色预测模型,并以添加最新数据和剔除最旧数据方式更新该预测模型。然后,利用超短期风速预测值,根据风电机组的风速功率特性曲线,计算其最大可发功率。实验结果表明,该方法在风电场有功功率分配周期内能够有效地估计机组最大可发功率；基于该方法的分配策略能够减少因机组调节裕量不足所引起的风电场有功功率跟踪误差。
4.针对在外部干扰及系统不确定性下的风电场有功功率输出控制问题,提出了一种基于滑模变结构的风电场有功功率控制算法。首先,根据机组的有功功率输出动态特性对风电场内所有运行机组进行等效化简,建立整个风电场的动态模型。然后,以风电场有功功率输出跟踪误差为参量构造滑模面,根据所建立的风电场模型,采用指数趋近律方法,设计风电场级控制器的变结构控制律,以实现风电场输出在外部干扰及系统不确定下对其参考值的精确跟踪。根据Lyapunov稳定性理论,证明了该算法能够使风电场有功功率输出跟踪误差在有限时间内快速地到达滑模面,并沿滑模面渐近收敛到零。实验结果表明,该控制器具有良好的动静态性能及鲁棒性。
With the development of large-scale wind farms, the wind-farm-generated power with the characteristic of randomness and volatile significantly influences the active power balance of power grid, which in turn seriously affects the stability and security of power grid. As the increasing penetration of the large-scale wind farm in power grid, the power system operator now require that wind farms have the active power control capability by implementing the active power control system.
The active power control system of wind farm generally has hierarchical structure with both a wind farm central control level and a wind turbine local control level. The performance of active power control system is affected by the active power control of each wind turbine local control level, the active power allocation and the active power control of the central wind farm level. Motivated by the ever-growing active power control requirements put on wind farm by power system operator, developing control method and allocation strategy for the active power control of wind farm has become the focus of ongoing research.
Due to the nonlinearity, strong coupling and uncertainty of wind turbine, and the uncertain disturbance from the complex wind farm environment, the performance of active power control system of wind farm is degraded. Thus, this dissertation focuses on the research of active power control of wind farm under the uncertainty mentioned above, and the main contributions of this dissertation are as follows:
1. A high gain observer based maximum power tracking control method is proposed for the wind turbine local control level. Firstly, a high gain observer is designed to estimate the aerodynamic torque, from which wind speed is deduced by using the Newton algorithm. With the estimated wind speed, a backstepping control based controller is then designed to achieve the maximum power point tracking. Based on Lyapunov stability theory, it is proved that the closed-loop control system is uniformly ultimately bounded with the tracking errors converging to the neighborhoods of the origin exponentially. The simulation results show that the proposed control method can effectively achieve maximum power point tracking without anemometer.
2. A nonlinear tracking differentiator based active power control method is proposed for wind turbine to track the power reference specified by the wind farm central control level. To avoid the adverse effects caused by power reference jump, a nonlinear tracking differentiator is designed to arrange a transient process for power reference. Then, a dynamic surface based adaptive controller is designed for the power tracking of wind turbine, and the parameter uncertainty caused by wind disturbance is compensated by an adaptive law, which is derived based on Lyapunov stability theory. The simulation results show that the proposed method can effectively reduce the adverse effects caused by power reference jump and wind disturbance.
3. An improved GM(1,1) based ultra-short term active power prediction method is proposed to provide the available power of wind turbine for the wind farm active power allocation. Firstly, the weaken buffering operator is utilized to preprocess the historical wind speed data and a GM(1,1) rolling model is built to predict wind speed from the preprocessed data. With the predicted wind speed, the available active power is then obtained according to the wind power curve. The experiment results show that the proposed method could effectively estimate the available power of each wind turbine, which can be used to optimize the wind farm active power allocation.
4. Considering the system uncertainty and wind disturbance, a sliding mode variable structure based active power control method is proposed for the wind farm central control level. First, the dynamic model of wind farm is established based on the analysis of the power tracking dynamic of the individual wind turbine. Secondly, by using the exponential reaching law method, the variable structure control law is designed for the controller in the wind farm central control level. Based on Lyapunov stability theory, it is proved that the power tracking error of wind farm will asymptotically converges to zero. The simulation results show that the proposed sliding mode controller provides good active power tracking performance and robustness against the system uncertainty and wind disturbance.
风电场有功功率控制系统一般采用由机组本地控制层和场级集中控制层组成的分层控制结构,其机组级的有功功率控制、风电场级有功功率分配和有功功率输出控制,是使风电场有功功率输出满足电网调度要求的关键。随着电网对风电场有功功率输出和系统稳定运行要求的不断提高,近年来,发展和完善风电场有功功率控制系统中的控制方法和分配策略已经成为风力发电控制技术研究的重点。
风电场有功功率控制系统在运行中,受风电机组本身的非线性、强耦合性和不确定性以及风电场环境中风速干扰等因素的影响,这些严重限制了风电场有功功率输出控制能力。因此,本文对在上述不确定性条件下的风电场有功功率控制方法进行了深入的研究。主要研究成果和创新点如下：
1.针对有效风速测量不准确引起的机组最大功率跟踪误差问题,研究了一种基于高增益观测器的风电机组最大功率跟踪控制方法。首先设计高增益观测器在线估计气动力矩,并采用Newton-Raphson算法计算当前风速的估计值,从而获取最优转速。然后对引入高增益观测器的系统,设计基于反演控制方法的转速控制器,通过直接控制发电机电压实现最优转速和最大功率的跟踪。利用Lyapunov理论证明了该控制系统是一致终结有界,且观测误差和跟踪误差收敛到原点的一个小邻域内。仿真实验结果表明,该方法无需风速测量,使风电机组能够对其最大可发功率进行有效跟踪。
2.针对有功功率参考值跃变和风速干扰下的机组恒功率输出控制问题,研究了一种基于非线性跟踪微分器的有功功率跟踪控制方法。首先,通过非线性跟踪微分器平滑场级集中控制层设定的有功功率参考信号,减少其跃变对系统动静态特性和稳定性的影响。然后设计基于动态面的自适应转速控制器,采用自适应控制方法在线补偿风速干扰引起的系统参数不确定性,并根据Lyapunov稳定性理论推导出动态面控制的控制律和不确定参数的调节律。仿真实验结果表明,该方法使风电机组在风速干扰下能够有效地跟踪具有跃变特性的有功功率参考信号。
3.针对风电场有功功率分配中的机组最大可发功率估计问题,提出了一种基于改进灰色模型的超短期有功功率预测方法。首先,通过缓冲算子对风速异常数据进行弱化处理,利用预处理后的风速历史数据,建立超短期风速灰色预测模型,并以添加最新数据和剔除最旧数据方式更新该预测模型。然后,利用超短期风速预测值,根据风电机组的风速功率特性曲线,计算其最大可发功率。实验结果表明,该方法在风电场有功功率分配周期内能够有效地估计机组最大可发功率；基于该方法的分配策略能够减少因机组调节裕量不足所引起的风电场有功功率跟踪误差。
4.针对在外部干扰及系统不确定性下的风电场有功功率输出控制问题,提出了一种基于滑模变结构的风电场有功功率控制算法。首先,根据机组的有功功率输出动态特性对风电场内所有运行机组进行等效化简,建立整个风电场的动态模型。然后,以风电场有功功率输出跟踪误差为参量构造滑模面,根据所建立的风电场模型,采用指数趋近律方法,设计风电场级控制器的变结构控制律,以实现风电场输出在外部干扰及系统不确定下对其参考值的精确跟踪。根据Lyapunov稳定性理论,证明了该算法能够使风电场有功功率输出跟踪误差在有限时间内快速地到达滑模面,并沿滑模面渐近收敛到零。实验结果表明,该控制器具有良好的动静态性能及鲁棒性。
With the development of large-scale wind farms, the wind-farm-generated power with the characteristic of randomness and volatile significantly influences the active power balance of power grid, which in turn seriously affects the stability and security of power grid. As the increasing penetration of the large-scale wind farm in power grid, the power system operator now require that wind farms have the active power control capability by implementing the active power control system.
The active power control system of wind farm generally has hierarchical structure with both a wind farm central control level and a wind turbine local control level. The performance of active power control system is affected by the active power control of each wind turbine local control level, the active power allocation and the active power control of the central wind farm level. Motivated by the ever-growing active power control requirements put on wind farm by power system operator, developing control method and allocation strategy for the active power control of wind farm has become the focus of ongoing research.
Due to the nonlinearity, strong coupling and uncertainty of wind turbine, and the uncertain disturbance from the complex wind farm environment, the performance of active power control system of wind farm is degraded. Thus, this dissertation focuses on the research of active power control of wind farm under the uncertainty mentioned above, and the main contributions of this dissertation are as follows:
1. A high gain observer based maximum power tracking control method is proposed for the wind turbine local control level. Firstly, a high gain observer is designed to estimate the aerodynamic torque, from which wind speed is deduced by using the Newton algorithm. With the estimated wind speed, a backstepping control based controller is then designed to achieve the maximum power point tracking. Based on Lyapunov stability theory, it is proved that the closed-loop control system is uniformly ultimately bounded with the tracking errors converging to the neighborhoods of the origin exponentially. The simulation results show that the proposed control method can effectively achieve maximum power point tracking without anemometer.
2. A nonlinear tracking differentiator based active power control method is proposed for wind turbine to track the power reference specified by the wind farm central control level. To avoid the adverse effects caused by power reference jump, a nonlinear tracking differentiator is designed to arrange a transient process for power reference. Then, a dynamic surface based adaptive controller is designed for the power tracking of wind turbine, and the parameter uncertainty caused by wind disturbance is compensated by an adaptive law, which is derived based on Lyapunov stability theory. The simulation results show that the proposed method can effectively reduce the adverse effects caused by power reference jump and wind disturbance.
3. An improved GM(1,1) based ultra-short term active power prediction method is proposed to provide the available power of wind turbine for the wind farm active power allocation. Firstly, the weaken buffering operator is utilized to preprocess the historical wind speed data and a GM(1,1) rolling model is built to predict wind speed from the preprocessed data. With the predicted wind speed, the available active power is then obtained according to the wind power curve. The experiment results show that the proposed method could effectively estimate the available power of each wind turbine, which can be used to optimize the wind farm active power allocation.
4. Considering the system uncertainty and wind disturbance, a sliding mode variable structure based active power control method is proposed for the wind farm central control level. First, the dynamic model of wind farm is established based on the analysis of the power tracking dynamic of the individual wind turbine. Secondly, by using the exponential reaching law method, the variable structure control law is designed for the controller in the wind farm central control level. Based on Lyapunov stability theory, it is proved that the power tracking error of wind farm will asymptotically converges to zero. The simulation results show that the proposed sliding mode controller provides good active power tracking performance and robustness against the system uncertainty and wind disturbance.
引文
[1]J. A. P. Lops, N. Hatziargyriou, J. Mutale, et al. Integrating distributed generation into electric power systems:A review of drives, challenges and opportunities [J]. Electric Power Systems Research,2007,77(9):1189-1203
[2]李泽椿,朱蓉,何晓凤,等.风能资源评估技术方法研究[J].气象学报,2007,65(5)：708-715
[3]王田,谢旭轩,高虎,等.英国可再生能源义务政策最新进展及我国的启示[J].可再生能源,2012,34(6)：32-36
[4]宋永华,孙静.未来欧洲的电网发展与电网技术[J].电力技术经济,2008,20(5)：1-5
[5]P. S(?)rensen, A. D. Hansen, F. Iov, et al. Wind farm models and control strategies[R]. Ris(?) National Laboratory,2005
[6]A. D. Hansen, P. S(?)rensen, F. Iov, et al. Grid support of a wind farm with active stall wind turbines and AC grid connection[J]. Wind Energy,2006,9(4):341-359
[7]J. Aho, A. Buckspan, J. Laks, et al. A tutorial of wind turbine control for supporting grid frequency through active power control[C]. American Control Conference, Motreal,2012, 3120-3131
[8]S. Liang, Q. Hu, W. Lee. A survey of harmonic emissions of a commercially operated Wind Farm[J]. IEEE Transactions on Industry Applications,2012,48(3):1115-1123
[9]C. S. Michael, L. J. Becky, A. S. Jeffrey, et al. The predominance of economic development in the support for large-scale wind farms in the U. S. great plains[J]. Renewable and Sustainable Energy Reviews,2012,16(6):3690-3701
[10]N. K. Detlefsen, P. E. Sorensen, P. B. Eriksen. Managing critical weather conditions in a large-scale wind based european power system-the twenties project[C]. IEEE Power and Energy Society General Meeting, Detroit,2011,1-5
[11]Y. Liu, K. Ari. Wind power in China:policy and development challenges[J]. Energy Policy, 2010,38(10):5520-5529
[12]B. Singh, S. N. Singh. Wind power interconnection into the power system:A review of grid code requirements[J]. The Electricity Journal,2009,22(5):54-63
[13]韩小琪,宋璇坤,李冰寒,等.风电出力变化对系统调频的影响[J].中国电力,2010,43(6)：26-29
[14]R. J. Barthelmie, K. Hansen, S. T. Frandsen, et al. Modeling and measuring flow and wind turbine wakes in large wind farm offshore[J]. Wind Energy,2009,12(5):431-444
[15]M. Tsili, S. Papathanassiou. A review of grid code technical requirements for wind farms[J]. IET Renewable Power Generation,2009,3(3):308-332
[16]M. A. Iriigo, A. Jon, L. M. Jose, et al. Connection requirements for wind farms:A survey on technical requirements and regulation[J]. Renewable and Sustainable Energy Reviews,2007, 11(8):1858-1872
[17]A. Morales, X. Robe, J. L. Rogriguez, et al. Advanced grid requirements for the integration of wind farms into the Spanish transmission system[J]. IET Renewable Power Generation,2007, 2(1):47-59
[18]饶建业,徐小东,何肇,等.中外风电并网技术规定对比[J].电网技术,2012,36(8)：44-49
[19]侯佑华,房大中,白永祥,等.大规模风电运行的调度模式设计[J].中国电力,2010,43(8)：67-72
[20]韩自奋,陈启卷.考虑约束的风电调度模式[J].电力系统自动化,2010,34(2)：89-92
[21]A. T. Le, K. Bhattacharya. Competitive framework for procurement of interruptible load serives[J]. IEEE Transactions on Power Systems,2003,18(2):889-897
[22]D. Mehdi, B. Jamel, R. Xavier. Hybrid solar-wind system with battery storage operating in grid-connected and standalone mode:control and energy management-experimental investigation[J]. Energy,2010,35(6):2587-2595
[23]A. D. Hansen, P. S(?)rensen, F. Iov, et al. Centralised power control of wind farm with doubly fed induction generators [J]. Renewable Energy,2006,31(7):935-951
[24]薛迎成,邰能灵,宋凯,等.基于分层架构的风电场频率调节策略[J].华东电力,2010,38(3)：0402-0407
[25]范高锋,赵海翔,王伟胜,等.基于恒速风电机组的风电场并网过程仿真[J].电网技术,2007,31(14)：20-23
[26]黄学良,刘志仁,祝瑞金,等.大容量变速恒频风电机组接入对电网运行的影响分析[J].电工技术学报,2010,25(4)：142-149
[27]Y. Sun, J. Lin, G. Li, et al. A survey on the grid integration of wind farms with variable speed wind plant systems[J]. Automation of Electric Power Systems,2010,34(3):75-80
[28]贺益康,胡家兵.双馈异步风力发电机并网运行中的几个热点问题[J].中国电机工程学报,2012,32(27)：1-15
[29]张仰飞,李海峰,王伟胜,等.直驱永磁同步风力发电机的电气参数辨识[J].电力系统自 动化,2012,36(14)：150-153
[30]韩金刚,陈昆明,汤天浩.半直驱永磁同步风力发电系统建模与电流解耦控制研究[J].电力系统保护与控制,2012,40(10)：100-115
[31]Z. Z. Zhang, J. X. Zou, G. Zheng, et al. Observer-based backstepping control of the half-direct permanent magnet wind power generation system[J]. Journal of Systems and Control Engineering,2012,226(4):441-450
[32]K. Raza, H. Goto, H. Guo, et al. A novel algorithem for fast and efficient maximum power point tracking of wind energy conversion systems[C]. Proceedings of the 2008 International Conference on Electrical Machines, Vilamoura,2008,1-6
[33]Q. Wang, L. Chang. An intelligent maximum power extraction algorithm for inverter-based variable speed wind turbine systems[J]. IEEE Transactions on Power Electronics,2003,19(5): 1242-1249
[34]张小莲,李群,殷明慧,等.一种引入停止机制的改进爬山算法[J].中国电机工程学报,2012,32(14)：128-134
[35]黄守道,佘峰,黄科元,等.三点比较法在风力发电系统中的应用[J].控制工程,2009,16(6)：764-767
[36]K. E. Johnson, L. J. Fingersh, M. J. Balas, et al. Methods for increasing region 2 power capture on a variable-speed wind turbine[J]. Journal of Solar Energy Engineering,2004, 126(4):1092-1100
[37]N. Wang, K. E. Johnson, A. D. Wright. FX-RLS-based feedforward control for LIDAR-enabled wind turbine load mitigation[J]. IEEE Transactions on Control Systems Technology,2012,20(5):1212-1222
[38]T. H. M. El-Fouly, E. F. El-Saadany, A. Y.Chikhani. Regulating wind turbine output power utilizing MEMs based actuators and sensors[C]. Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering, Montreal,2003:595-598
[39]V. Galdi, A. Piccolo, P. Siano. Designing an adaptive fuzzy controller for maximum wind energy extraction[J]. IEEE Transactions on Energy Conversion,2008,23(2):559-569
[40]A. L. Elshafei, H. Emara. Evaluation of fuzzy-based maximum power-tracking in wind energy conversion systems[J]. IET Renewable Power Generation,2011,5(6):422-430
[41]M. G Simoes, B. K. Bose, R. J. Spiegel. Fuzzy logic based intelligent control of a variable speed cage machine wind generation system[J]. IEEE Transactions on Power Electronics, 1997,12(1):87-95
[42]M. Pucci, M. Cirrincione. Neural MPPT control of wind generators with induction machines without speed sensor[J]. IEEE Transactions on Industrial Electronics,2011,58(1):37-47
[43]Y. D. Song. Control of wind turbines using memory-based method[J]. Journal of Wind Engineering and Industrial Aerodynamics,2000,85(3):263-275
[44]E. Iyasere, M. Salah, D. Dawson, et al. Optimum seeking-based non-linear controller to maximise energy capture in a variable speed wind turbine[J]. IET Control Theory & Applications,2012,6(4):526-532
[45]C. Y. Tang, Y. Guo, J. N. Jiang. Nonlinear dual-mode control of variable-speed wind turbines with doubly fed induction generators[J]. IEEE Transactions on Control Systems Technology, 2011,19(4):744-756
[46]O. Soares, H. Goncalves, A. Martins, et al. Nonlinear control of the doubly-fed induction generator in wind power systems[J]. Renewable Energy,2010,35(8):1662-1670
[47]B. Beltran, M. E. H. Benbouzid, T. Ahmed-Ali. Second-order sliding mode control of a doubly fed induction generator driven wind turbine[J]. IEEE Transactions on Energy Conversion, 2012,27(2):261-269
[48]Q. Wei, Y. Xu, G. Xiang. Wind speed and rotor position sensorless control for direct-drive PMG wind turbines[J]. IEEE Transactions on Industrial Applications,2012,48(1):3-11
[49]B. Beltran, T. Ahmed-Ali, M. Benbouzid. High-order sliding-mode control of variable-speed wind turbines[J]. IEEE Transactions on Industrial Electronics,2009,56(9):3314-3321
[50]L. Y. Pao, K. E. Johnson, A tutorial on the dynamics and control of wind turbines and wind farms[C]. American Control Conference, St. Louis,2009,2076-2089
[51]T. L. Pan, Z. C. Ji, Z. H. Jiang. Maximum power point tracking of wind energy conversion systems based on sliding mode extremum seeking control[C]. Energy 2030 Conference, Atlanta,2008,1-5
[52]E. B. Muhando, T. Senjyu, A. Yona, et al. Disturbance rejection by dual pitch control and self-tuning regulator for wind turbine generator parametric uncertainty compensation[J]. IET Transactions on Control Theory & Applications,2007,1(5):1431-1440
[53]李晶,宋家骅,王伟胜.大型变速恒频风力发电机组建模与仿真[J].中国电机工程学报,2004,24(6)：100-105
[54]沈艳霞,朱芸,纪志成.LPV动态补偿的风能转换系统变桨距控制[J].控制理论与应用,2009,26(11)：1282-1288
[55]张先勇,吴捷,杨金明,等.额定风速以上风力发电机组的恒功率H∞鲁棒控制[J].控制 理论与应用,2008,25(2)：321-328
[56]R. Rocha, L. S. M. Filho. A mutltivariable Hoo control for wind energy conversion system [C]. Proceedings of 2003 IEEE Conference on Control Applications, Istanbul,2003,206-211
[57]刘吉宏,柳亦兵,徐大平.基于反馈线性化H∞理论的风电系统桨距角控制[J].系统仿真学报,2010,22(6)：1416-1420
[58]K. A. Stol, M. J. Balas. Periodic disturbance accommodating control for blade load mitigation in wind turbines[J]. The Transactions of the ASME Journal of Solar Engineering,2003,125(4): 379-385
[59]H. Geng, G. Yang. Output power control for variable-speed variable-pitch wind generation systems[J]. IEEE Transactions on energy conversion,2010,25(2):494-503
[60]夏长亮,宋战锋.变速恒频风力发电系统变桨距自抗扰控制[J].中国电机工程学报,2007,27(14)：91-95
[61]L. M. Fernandez, C. A. Garcia, F. Jurado. Comparative study on the performance of control systems for doubly fed induction generator (dfig) wind turbines operating with power regulation[J]. Energy,2008,33(9):1438-1452
[62]B. Boukhezzar, H. Siguerdidjane. Nonlinear control of variable speed wind turbines for power regulation[C]. Proceedings of 2005 IEEE Conference on Control Applications, Tornoto,2005, 114-119
[63]B. Beltran, T. Ahmed-Ali, M. Benbouzid. Sliding mode power control of variable-speed wind energy conversion systems[J]. IEEE Transactions on Energy Conversion,2008,23(2): 551-558
[64]T. Senjyu, R. Sakamoto, N. Urasaki, et al. Output power leveling of wind turbine generator for all operating regions by pitch angle control[J]. IEEE Transactions on Energy Conversion,2006, 21(2):467-475.
[65]J. L. Rodriguez, J. C. Burgos. Automatic generation of a wind farm with variable speed wind turbines[J]. IEEE Transactions on Energy Conversion,2002,17(2):279-284
[66]谷峰,梁军,张利.并网双馈发电机风电场的功率控制[C].中国高等学校电力系统及其自动化专业第二十四届学术年会论文集,北京,2008,2734-2737
[67]邹见效,李丹,郑刚,等.基于机组状态分类的风电场有功功率控制策略[J].电力系统自动化,2011,35(24)：28-32
[68]J. X. Zou, J. P. Yao, Q. Z. Zou, et al. A multi-objective optimization approach to active power control of wind farms[C]. American Control Conference, Montreal,2012,4381-4386.
[69]R. G. de Almeida, E. D. Castronuovo, J. A. Pecas Lopes. Optimum generation control in wind parks when carrying out system operator requests[J]. IEEE Transaction on Power Systems, 2006,21(2):718-725
[70]C. F. Moyano, J. A. Pecas Lopes. An optimization approach for wind turbine commitment and dispatch in a wind park[J]. Electric Power Systems Research,2009,79(1):71-79
[71]M. Soleimanzadeh, R. Wisniewski. Controller design for a wind farm, considering both power and load aspects[J]. Mechatronics,2011,21(4):720-727
[72]V. Spudic, M. Jelavic, M. Baotic, et al. Hierarchical wind farm control for power/load optimization[C]. The Science of Making Torque from Wind (Torque2010), Heraklion,2010, 1-8
[73]D. Madjidian, K. Martensson, A. Rantzer. A distributed power coordination scheme for fatigue load reduction in wind farms[C]. American Control Conference, San Francisco,2011, 5219-5224
[74]张利,王成福,牛远方.风电场输出有功功率的协调分配策略[J].电力自动化设备,2012,32(8)：101-105
[75]黄崇鑫,张凯锋,戴先中,等.考虑DFIG机组容量限制的风电场功率分配方法[J].电力系统保护与控制,2010,38(21)：202-207
[76]L. Chang, Y. Yin. Strategies for operating wind power in a similar manner of conventional power plant[J]. IEEE Transactions on Energy Conversion,2009,24(4):926-934
[77]尹远,卢继平,刘钢,等.基于DFIG机组转子动能的风电场有功功率优化分配方法[J].电力系统保护与控制,2012,40(17)：127-132
[78]L. Ma, S. Y. Luan, C. W. Jiang, etc. A review on the forecasting of wind speed and generated power[J]. Renewable and Sustainable Energy Reviews,2009,13(4):915-920
[79]T. H. M. El-Fouly, E. F. El-Saadany, M. M. A. Salama. One Day Ahead Prediction of Wind Speed and Direction [J]. IEEE Transactions Energy Conversion,2008,23(1):191-201
[80]S. Rajagopalan, S. Santoso. Wind power forecasting and error analysis using the autoregressive moving average modeling[C]. IEEE Power & Energy Society General Meeting, Calgary,2009,1-6
[81]G. Papaefthymiou, P. Pinson. Modeling of spatial dependence in wind power forecast uncertainty[C]. Proceedings of International Conference on Probabilistic Methods Applied to Power Systems, Singapore,2008,1-9
[82]P. Flores, A. Tapia, G Tapia. Application of a control algorithm for wind speed prediction and active power generation[J]. Renewable Energy,2005,30(4):523-536
[83]I. G. Damousis, M. C. Alexiadis, J. B. Theocharis, et al. A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation[J]. IEEE Transactions on Energy Conversion,2004,19(2):352-361
[84]G. Sideratos, N. D. Hatziargyriou. An advanced statistical method for wind power forecasting[J]. IEEE Transactions on Power Systems,2007,22(1):258-265
[85]V. Akhmatov, H. Knudsen. Modeling and transient stability of large wind farms[J]. Electrical Power and Energy System,2003,25(1):123-144
[86]V. Akhmatov. Analysis of dynamic behavior of electric power systems with large amount of wind power[D]. Denmark:Technical University of Denmark,2003
[87]李先允,陈小虎,唐国庆.大型风力发电场等值建模研究综述[J].华北电力大学学报,2006,33(1)：42-46
[88]J. Daniel, A. Gentile, et al. Fixed-speed wind-generator and wind-park modeling for transient stability studies[J]. IEEE Transactions on Power Systems,2004,19(4):1911-1917
[89]T. Boehme, A. R. Wallace, G. P. Harrison, Applying time series to power flow analysis in networks with high wind penetration[J]. IEEE Transactions on Power Systems,2007,22(3): 951-957
[90]A. E. Feijoo, J. Cidras. Modeling of wind farms in the load flow analysis[J]. IEEE Transactions on Power Systems,2000,15(1):110-115
[91]X. Wang, Z. Chen, Z. Yuan. Output tracking based on extended observer for nonlinear uncertain system[J]. Control and Decision,2004,19(10):1113-1116
[92]H. Geng, D. Xu. Stability analysis and improvements for variable-speed multipole permanent magnet synchronous generator-based wind energy conversion system[J]. IEEE Transactions on Sustainable Energy,2011,2(4):459-467
[93]B. Boukhezzar, H. Siguerdidjane, M. Maureen Hand. Nonlinear control of variable-Speed Wind Turbines Generator Torque Limiting and Power Optimization[J]. Journal of Solar Energy Engineering,2006,128(4):516-530
[94]A. N. Atassi, H. K. Khalil. Seperation results for the stabilization of nonlinear systems using different high-gain observer designs[J]. Systems and Control Letters,2000,39(3):183-191
[95]J. Q. Han, From pid to active disturbance rejection control[J]. IEEE Transactions on Industrial Electronics,2009,56 (3):900-906
[96]D. Wang, J. Huang. Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form[J]. IEEE Transactions on Neural Networks, 2005,16(1):195-202
[97]国家电网公司.Q/GDW_588-2011.国家电网公司企业标准一风电功率预测功能规范[S].北京：中国电力出版社,2011年3月3日
[98]Q. W. Sun, X. H. Luan. The study of cluster prediction method on sales forecast based on residual error modified GM (1,1)[J]. Business and Information Management,2008,2(9): 246-249
[99]吴正朋,刘思峰.弱化缓冲算子性质研究[J].控制与决策,2010,25(6)：958-960
[100]A. W. L. Yao, S. C. Chi, J. H. Chen. An improved grey-based approach for electricity demand forecasting [J]. Electric Power Systems Research,2003,67(3):217-224
[101]T. H. M. El-Fouly, E. F. El-Saadany, M. M. A. Salama. Improved grey predictor rolling models for wind power prediction [J]. IET Generation, Transmission & Distribution,2007,1(6): 928-937
[102]Z. Z. Zhang, H. B. Xu, et al. Sliding mode control based active power control for wind farm with variable speed wind generation system[J]. Journal of Mechanical Engineering Science, Proc. IMechE, Part C,2013,227(3):449-458
[103]V. Utkin. Variable structure control systems with sliding mode[J]. IEEE Transactions on Automatic Control,1977,22(1):212-222
[104]J. Y. Hung, W. B. Gao, J. C. Hung. Variable structure control:A survey[J]. IEEE Transactions on Industrial Electronics,1993,40(1):2-22
[105]W. B. Gao, J. C. Hung. Variable structure control of nonlinear systems:a new approach[J]. IEEE Transactions on Industrial Electronics,1993,40(1):45-55
[106]Y. Lin, H. Liu, R. R. Costa, et al. A variable structure model reference robust control without a prior knowledge of high frequency gain sign[J]. Automatica,2008,44(4):1036-1044
[107]Q. Sun, Y. Wang. Intelligent sliding mode control of nonlinear systems based on cloud models[C].29th Chinese Control Conference (CCC), Beijing,2010:2531-2535
[108]J. B. Hu, J. Chu. A continuous and differentiable sliding-mode variable structure controller and its simulation. Journal of System Simulation,2000,12(4):396-399
[2]李泽椿,朱蓉,何晓凤,等.风能资源评估技术方法研究[J].气象学报,2007,65(5)：708-715
[3]王田,谢旭轩,高虎,等.英国可再生能源义务政策最新进展及我国的启示[J].可再生能源,2012,34(6)：32-36
[4]宋永华,孙静.未来欧洲的电网发展与电网技术[J].电力技术经济,2008,20(5)：1-5
[5]P. S(?)rensen, A. D. Hansen, F. Iov, et al. Wind farm models and control strategies[R]. Ris(?) National Laboratory,2005
[6]A. D. Hansen, P. S(?)rensen, F. Iov, et al. Grid support of a wind farm with active stall wind turbines and AC grid connection[J]. Wind Energy,2006,9(4):341-359
[7]J. Aho, A. Buckspan, J. Laks, et al. A tutorial of wind turbine control for supporting grid frequency through active power control[C]. American Control Conference, Motreal,2012, 3120-3131
[8]S. Liang, Q. Hu, W. Lee. A survey of harmonic emissions of a commercially operated Wind Farm[J]. IEEE Transactions on Industry Applications,2012,48(3):1115-1123
[9]C. S. Michael, L. J. Becky, A. S. Jeffrey, et al. The predominance of economic development in the support for large-scale wind farms in the U. S. great plains[J]. Renewable and Sustainable Energy Reviews,2012,16(6):3690-3701
[10]N. K. Detlefsen, P. E. Sorensen, P. B. Eriksen. Managing critical weather conditions in a large-scale wind based european power system-the twenties project[C]. IEEE Power and Energy Society General Meeting, Detroit,2011,1-5
[11]Y. Liu, K. Ari. Wind power in China:policy and development challenges[J]. Energy Policy, 2010,38(10):5520-5529
[12]B. Singh, S. N. Singh. Wind power interconnection into the power system:A review of grid code requirements[J]. The Electricity Journal,2009,22(5):54-63
[13]韩小琪,宋璇坤,李冰寒,等.风电出力变化对系统调频的影响[J].中国电力,2010,43(6)：26-29
[14]R. J. Barthelmie, K. Hansen, S. T. Frandsen, et al. Modeling and measuring flow and wind turbine wakes in large wind farm offshore[J]. Wind Energy,2009,12(5):431-444
[15]M. Tsili, S. Papathanassiou. A review of grid code technical requirements for wind farms[J]. IET Renewable Power Generation,2009,3(3):308-332
[16]M. A. Iriigo, A. Jon, L. M. Jose, et al. Connection requirements for wind farms:A survey on technical requirements and regulation[J]. Renewable and Sustainable Energy Reviews,2007, 11(8):1858-1872
[17]A. Morales, X. Robe, J. L. Rogriguez, et al. Advanced grid requirements for the integration of wind farms into the Spanish transmission system[J]. IET Renewable Power Generation,2007, 2(1):47-59
[18]饶建业,徐小东,何肇,等.中外风电并网技术规定对比[J].电网技术,2012,36(8)：44-49
[19]侯佑华,房大中,白永祥,等.大规模风电运行的调度模式设计[J].中国电力,2010,43(8)：67-72
[20]韩自奋,陈启卷.考虑约束的风电调度模式[J].电力系统自动化,2010,34(2)：89-92
[21]A. T. Le, K. Bhattacharya. Competitive framework for procurement of interruptible load serives[J]. IEEE Transactions on Power Systems,2003,18(2):889-897
[22]D. Mehdi, B. Jamel, R. Xavier. Hybrid solar-wind system with battery storage operating in grid-connected and standalone mode:control and energy management-experimental investigation[J]. Energy,2010,35(6):2587-2595
[23]A. D. Hansen, P. S(?)rensen, F. Iov, et al. Centralised power control of wind farm with doubly fed induction generators [J]. Renewable Energy,2006,31(7):935-951
[24]薛迎成,邰能灵,宋凯,等.基于分层架构的风电场频率调节策略[J].华东电力,2010,38(3)：0402-0407
[25]范高锋,赵海翔,王伟胜,等.基于恒速风电机组的风电场并网过程仿真[J].电网技术,2007,31(14)：20-23
[26]黄学良,刘志仁,祝瑞金,等.大容量变速恒频风电机组接入对电网运行的影响分析[J].电工技术学报,2010,25(4)：142-149
[27]Y. Sun, J. Lin, G. Li, et al. A survey on the grid integration of wind farms with variable speed wind plant systems[J]. Automation of Electric Power Systems,2010,34(3):75-80
[28]贺益康,胡家兵.双馈异步风力发电机并网运行中的几个热点问题[J].中国电机工程学报,2012,32(27)：1-15
[29]张仰飞,李海峰,王伟胜,等.直驱永磁同步风力发电机的电气参数辨识[J].电力系统自 动化,2012,36(14)：150-153
[30]韩金刚,陈昆明,汤天浩.半直驱永磁同步风力发电系统建模与电流解耦控制研究[J].电力系统保护与控制,2012,40(10)：100-115
[31]Z. Z. Zhang, J. X. Zou, G. Zheng, et al. Observer-based backstepping control of the half-direct permanent magnet wind power generation system[J]. Journal of Systems and Control Engineering,2012,226(4):441-450
[32]K. Raza, H. Goto, H. Guo, et al. A novel algorithem for fast and efficient maximum power point tracking of wind energy conversion systems[C]. Proceedings of the 2008 International Conference on Electrical Machines, Vilamoura,2008,1-6
[33]Q. Wang, L. Chang. An intelligent maximum power extraction algorithm for inverter-based variable speed wind turbine systems[J]. IEEE Transactions on Power Electronics,2003,19(5): 1242-1249
[34]张小莲,李群,殷明慧,等.一种引入停止机制的改进爬山算法[J].中国电机工程学报,2012,32(14)：128-134
[35]黄守道,佘峰,黄科元,等.三点比较法在风力发电系统中的应用[J].控制工程,2009,16(6)：764-767
[36]K. E. Johnson, L. J. Fingersh, M. J. Balas, et al. Methods for increasing region 2 power capture on a variable-speed wind turbine[J]. Journal of Solar Energy Engineering,2004, 126(4):1092-1100
[37]N. Wang, K. E. Johnson, A. D. Wright. FX-RLS-based feedforward control for LIDAR-enabled wind turbine load mitigation[J]. IEEE Transactions on Control Systems Technology,2012,20(5):1212-1222
[38]T. H. M. El-Fouly, E. F. El-Saadany, A. Y.Chikhani. Regulating wind turbine output power utilizing MEMs based actuators and sensors[C]. Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering, Montreal,2003:595-598
[39]V. Galdi, A. Piccolo, P. Siano. Designing an adaptive fuzzy controller for maximum wind energy extraction[J]. IEEE Transactions on Energy Conversion,2008,23(2):559-569
[40]A. L. Elshafei, H. Emara. Evaluation of fuzzy-based maximum power-tracking in wind energy conversion systems[J]. IET Renewable Power Generation,2011,5(6):422-430
[41]M. G Simoes, B. K. Bose, R. J. Spiegel. Fuzzy logic based intelligent control of a variable speed cage machine wind generation system[J]. IEEE Transactions on Power Electronics, 1997,12(1):87-95
[42]M. Pucci, M. Cirrincione. Neural MPPT control of wind generators with induction machines without speed sensor[J]. IEEE Transactions on Industrial Electronics,2011,58(1):37-47
[43]Y. D. Song. Control of wind turbines using memory-based method[J]. Journal of Wind Engineering and Industrial Aerodynamics,2000,85(3):263-275
[44]E. Iyasere, M. Salah, D. Dawson, et al. Optimum seeking-based non-linear controller to maximise energy capture in a variable speed wind turbine[J]. IET Control Theory & Applications,2012,6(4):526-532
[45]C. Y. Tang, Y. Guo, J. N. Jiang. Nonlinear dual-mode control of variable-speed wind turbines with doubly fed induction generators[J]. IEEE Transactions on Control Systems Technology, 2011,19(4):744-756
[46]O. Soares, H. Goncalves, A. Martins, et al. Nonlinear control of the doubly-fed induction generator in wind power systems[J]. Renewable Energy,2010,35(8):1662-1670
[47]B. Beltran, M. E. H. Benbouzid, T. Ahmed-Ali. Second-order sliding mode control of a doubly fed induction generator driven wind turbine[J]. IEEE Transactions on Energy Conversion, 2012,27(2):261-269
[48]Q. Wei, Y. Xu, G. Xiang. Wind speed and rotor position sensorless control for direct-drive PMG wind turbines[J]. IEEE Transactions on Industrial Applications,2012,48(1):3-11
[49]B. Beltran, T. Ahmed-Ali, M. Benbouzid. High-order sliding-mode control of variable-speed wind turbines[J]. IEEE Transactions on Industrial Electronics,2009,56(9):3314-3321
[50]L. Y. Pao, K. E. Johnson, A tutorial on the dynamics and control of wind turbines and wind farms[C]. American Control Conference, St. Louis,2009,2076-2089
[51]T. L. Pan, Z. C. Ji, Z. H. Jiang. Maximum power point tracking of wind energy conversion systems based on sliding mode extremum seeking control[C]. Energy 2030 Conference, Atlanta,2008,1-5
[52]E. B. Muhando, T. Senjyu, A. Yona, et al. Disturbance rejection by dual pitch control and self-tuning regulator for wind turbine generator parametric uncertainty compensation[J]. IET Transactions on Control Theory & Applications,2007,1(5):1431-1440
[53]李晶,宋家骅,王伟胜.大型变速恒频风力发电机组建模与仿真[J].中国电机工程学报,2004,24(6)：100-105
[54]沈艳霞,朱芸,纪志成.LPV动态补偿的风能转换系统变桨距控制[J].控制理论与应用,2009,26(11)：1282-1288
[55]张先勇,吴捷,杨金明,等.额定风速以上风力发电机组的恒功率H∞鲁棒控制[J].控制 理论与应用,2008,25(2)：321-328
[56]R. Rocha, L. S. M. Filho. A mutltivariable Hoo control for wind energy conversion system [C]. Proceedings of 2003 IEEE Conference on Control Applications, Istanbul,2003,206-211
[57]刘吉宏,柳亦兵,徐大平.基于反馈线性化H∞理论的风电系统桨距角控制[J].系统仿真学报,2010,22(6)：1416-1420
[58]K. A. Stol, M. J. Balas. Periodic disturbance accommodating control for blade load mitigation in wind turbines[J]. The Transactions of the ASME Journal of Solar Engineering,2003,125(4): 379-385
[59]H. Geng, G. Yang. Output power control for variable-speed variable-pitch wind generation systems[J]. IEEE Transactions on energy conversion,2010,25(2):494-503
[60]夏长亮,宋战锋.变速恒频风力发电系统变桨距自抗扰控制[J].中国电机工程学报,2007,27(14)：91-95
[61]L. M. Fernandez, C. A. Garcia, F. Jurado. Comparative study on the performance of control systems for doubly fed induction generator (dfig) wind turbines operating with power regulation[J]. Energy,2008,33(9):1438-1452
[62]B. Boukhezzar, H. Siguerdidjane. Nonlinear control of variable speed wind turbines for power regulation[C]. Proceedings of 2005 IEEE Conference on Control Applications, Tornoto,2005, 114-119
[63]B. Beltran, T. Ahmed-Ali, M. Benbouzid. Sliding mode power control of variable-speed wind energy conversion systems[J]. IEEE Transactions on Energy Conversion,2008,23(2): 551-558
[64]T. Senjyu, R. Sakamoto, N. Urasaki, et al. Output power leveling of wind turbine generator for all operating regions by pitch angle control[J]. IEEE Transactions on Energy Conversion,2006, 21(2):467-475.
[65]J. L. Rodriguez, J. C. Burgos. Automatic generation of a wind farm with variable speed wind turbines[J]. IEEE Transactions on Energy Conversion,2002,17(2):279-284
[66]谷峰,梁军,张利.并网双馈发电机风电场的功率控制[C].中国高等学校电力系统及其自动化专业第二十四届学术年会论文集,北京,2008,2734-2737
[67]邹见效,李丹,郑刚,等.基于机组状态分类的风电场有功功率控制策略[J].电力系统自动化,2011,35(24)：28-32
[68]J. X. Zou, J. P. Yao, Q. Z. Zou, et al. A multi-objective optimization approach to active power control of wind farms[C]. American Control Conference, Montreal,2012,4381-4386.
[69]R. G. de Almeida, E. D. Castronuovo, J. A. Pecas Lopes. Optimum generation control in wind parks when carrying out system operator requests[J]. IEEE Transaction on Power Systems, 2006,21(2):718-725
[70]C. F. Moyano, J. A. Pecas Lopes. An optimization approach for wind turbine commitment and dispatch in a wind park[J]. Electric Power Systems Research,2009,79(1):71-79
[71]M. Soleimanzadeh, R. Wisniewski. Controller design for a wind farm, considering both power and load aspects[J]. Mechatronics,2011,21(4):720-727
[72]V. Spudic, M. Jelavic, M. Baotic, et al. Hierarchical wind farm control for power/load optimization[C]. The Science of Making Torque from Wind (Torque2010), Heraklion,2010, 1-8
[73]D. Madjidian, K. Martensson, A. Rantzer. A distributed power coordination scheme for fatigue load reduction in wind farms[C]. American Control Conference, San Francisco,2011, 5219-5224
[74]张利,王成福,牛远方.风电场输出有功功率的协调分配策略[J].电力自动化设备,2012,32(8)：101-105
[75]黄崇鑫,张凯锋,戴先中,等.考虑DFIG机组容量限制的风电场功率分配方法[J].电力系统保护与控制,2010,38(21)：202-207
[76]L. Chang, Y. Yin. Strategies for operating wind power in a similar manner of conventional power plant[J]. IEEE Transactions on Energy Conversion,2009,24(4):926-934
[77]尹远,卢继平,刘钢,等.基于DFIG机组转子动能的风电场有功功率优化分配方法[J].电力系统保护与控制,2012,40(17)：127-132
[78]L. Ma, S. Y. Luan, C. W. Jiang, etc. A review on the forecasting of wind speed and generated power[J]. Renewable and Sustainable Energy Reviews,2009,13(4):915-920
[79]T. H. M. El-Fouly, E. F. El-Saadany, M. M. A. Salama. One Day Ahead Prediction of Wind Speed and Direction [J]. IEEE Transactions Energy Conversion,2008,23(1):191-201
[80]S. Rajagopalan, S. Santoso. Wind power forecasting and error analysis using the autoregressive moving average modeling[C]. IEEE Power & Energy Society General Meeting, Calgary,2009,1-6
[81]G. Papaefthymiou, P. Pinson. Modeling of spatial dependence in wind power forecast uncertainty[C]. Proceedings of International Conference on Probabilistic Methods Applied to Power Systems, Singapore,2008,1-9
[82]P. Flores, A. Tapia, G Tapia. Application of a control algorithm for wind speed prediction and active power generation[J]. Renewable Energy,2005,30(4):523-536
[83]I. G. Damousis, M. C. Alexiadis, J. B. Theocharis, et al. A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation[J]. IEEE Transactions on Energy Conversion,2004,19(2):352-361
[84]G. Sideratos, N. D. Hatziargyriou. An advanced statistical method for wind power forecasting[J]. IEEE Transactions on Power Systems,2007,22(1):258-265
[85]V. Akhmatov, H. Knudsen. Modeling and transient stability of large wind farms[J]. Electrical Power and Energy System,2003,25(1):123-144
[86]V. Akhmatov. Analysis of dynamic behavior of electric power systems with large amount of wind power[D]. Denmark:Technical University of Denmark,2003
[87]李先允,陈小虎,唐国庆.大型风力发电场等值建模研究综述[J].华北电力大学学报,2006,33(1)：42-46
[88]J. Daniel, A. Gentile, et al. Fixed-speed wind-generator and wind-park modeling for transient stability studies[J]. IEEE Transactions on Power Systems,2004,19(4):1911-1917
[89]T. Boehme, A. R. Wallace, G. P. Harrison, Applying time series to power flow analysis in networks with high wind penetration[J]. IEEE Transactions on Power Systems,2007,22(3): 951-957
[90]A. E. Feijoo, J. Cidras. Modeling of wind farms in the load flow analysis[J]. IEEE Transactions on Power Systems,2000,15(1):110-115
[91]X. Wang, Z. Chen, Z. Yuan. Output tracking based on extended observer for nonlinear uncertain system[J]. Control and Decision,2004,19(10):1113-1116
[92]H. Geng, D. Xu. Stability analysis and improvements for variable-speed multipole permanent magnet synchronous generator-based wind energy conversion system[J]. IEEE Transactions on Sustainable Energy,2011,2(4):459-467
[93]B. Boukhezzar, H. Siguerdidjane, M. Maureen Hand. Nonlinear control of variable-Speed Wind Turbines Generator Torque Limiting and Power Optimization[J]. Journal of Solar Energy Engineering,2006,128(4):516-530
[94]A. N. Atassi, H. K. Khalil. Seperation results for the stabilization of nonlinear systems using different high-gain observer designs[J]. Systems and Control Letters,2000,39(3):183-191
[95]J. Q. Han, From pid to active disturbance rejection control[J]. IEEE Transactions on Industrial Electronics,2009,56 (3):900-906
[96]D. Wang, J. Huang. Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form[J]. IEEE Transactions on Neural Networks, 2005,16(1):195-202
[97]国家电网公司.Q/GDW_588-2011.国家电网公司企业标准一风电功率预测功能规范[S].北京：中国电力出版社,2011年3月3日
[98]Q. W. Sun, X. H. Luan. The study of cluster prediction method on sales forecast based on residual error modified GM (1,1)[J]. Business and Information Management,2008,2(9): 246-249
[99]吴正朋,刘思峰.弱化缓冲算子性质研究[J].控制与决策,2010,25(6)：958-960
[100]A. W. L. Yao, S. C. Chi, J. H. Chen. An improved grey-based approach for electricity demand forecasting [J]. Electric Power Systems Research,2003,67(3):217-224
[101]T. H. M. El-Fouly, E. F. El-Saadany, M. M. A. Salama. Improved grey predictor rolling models for wind power prediction [J]. IET Generation, Transmission & Distribution,2007,1(6): 928-937
[102]Z. Z. Zhang, H. B. Xu, et al. Sliding mode control based active power control for wind farm with variable speed wind generation system[J]. Journal of Mechanical Engineering Science, Proc. IMechE, Part C,2013,227(3):449-458
[103]V. Utkin. Variable structure control systems with sliding mode[J]. IEEE Transactions on Automatic Control,1977,22(1):212-222
[104]J. Y. Hung, W. B. Gao, J. C. Hung. Variable structure control:A survey[J]. IEEE Transactions on Industrial Electronics,1993,40(1):2-22
[105]W. B. Gao, J. C. Hung. Variable structure control of nonlinear systems:a new approach[J]. IEEE Transactions on Industrial Electronics,1993,40(1):45-55
[106]Y. Lin, H. Liu, R. R. Costa, et al. A variable structure model reference robust control without a prior knowledge of high frequency gain sign[J]. Automatica,2008,44(4):1036-1044
[107]Q. Sun, Y. Wang. Intelligent sliding mode control of nonlinear systems based on cloud models[C].29th Chinese Control Conference (CCC), Beijing,2010:2531-2535
[108]J. B. Hu, J. Chu. A continuous and differentiable sliding-mode variable structure controller and its simulation. Journal of System Simulation,2000,12(4):396-399
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |