[1] Cao S Q, Hao W J, Hao S Y et al. Maximum power tracking control of wind turbine based on super twisting optimization algorithm[J]. Power System Protection and Control, 47, 61-68(2019).

[2] Zhuang Z B, Liu X Y, Chen X. Estimation method of turbulent wind speed based on lidar pulse characteristics[J]. Infrared and Laser Engineering, 47, 1106001(2018).

[3] Han B, Zhou L W, Chen H et al. Approach to model predictive control of large wind turbine using light detection and ranging measurements[J]. Proceedings of the CSEE, 36, 5062-5069(2016).

[4] Laks J, Pao L, Simley E et al. Model predictive control using preview measurements from LIDAR. [C]∥49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. Reston, Virginia: AIAA(2011).

[5] Wang P C, Chen T D, Zhou A R et al. Wind velocity estimation algorithm based on Gaussian fitting in coherent lidar[J]. Infrared and Laser Engineering, 47, 107-112(2018).

[6] Han B, Zhou L W, Zhang Z W. LIDAR-assisted radial basis function neural network optimization for wind turbines[J]. IEEJ Transactions on Electrical and Electronic Engineering, 13, 195-200(2018).

[7] Giyanani A. Bierbooms W M, van Bussel G J W. Estimation of rotor effective wind speeds using autoregressive models on Lidar data[J]. Journal of Physics: Conference Series, 753, 072018(2016).

[8] Feng C Z, Wu S H, Liu B Y. Gluing method of detected signal based on lidar[J]. Acta Photonica Sinica, 47, 0601001(2018).

[9] Wang P C. Signal processing and algorithm research for coherent wind lidar[D]. Hefei: University of Science and Technology of China(2018).

[10] Simley E, Pao L, Kelley N et al. LIDAR wind speed measurements of evolving wind fields. [C]∥50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. Reston, Virginia: AIAA(2012).

[11] Yin L, Liu S, Gao H. Regularised estimation for ARMAX process with measurements subject to outliers[J]. Iet Control Theory and Applications, 12, 865-874(2018).

[12] Uddin F, Tufa L D. Yousif S M T, et al. Comparison of ARX and ARMAX decorrelation models for detecting model-plant mismatch[J]. Procedia Engineering, 148, 985-991(2016).

[13] Yu H, Yuan J, Yu X Y et al. Tracking prediction model for P M2.5 hourly concentration based on ARMAX[J]. Journal of Tianjin University (Science and Technology), 50, 105-111(2017).

[14] Zhao B B, Wang Y, Wang B et al. Photovoltaic power prediction in distribution network based on ARIMA model time series[J]. Renewable Energy Resources, 37, 820-823(2019).

[15] Wang B, Tai N L, Zhai H Q et al. A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting[J]. Electric Power Systems Research, 78, 1679-1685(2008).

[16] Peng D G, Chen Y W, Qian Y L et al. Transformer winding temperature soft measurement model based on particle swarm optimization-support vector regression[J]. Transactions of China Electrotechnical Society, 33, 1742-1749,1761(2018).

[17] Mérida J, Aguilar L T, Dávila J. Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization[J]. Renewable Energy, 71, 715-728(2014).

[18] Boukhezzar B, Siguerdidjane H. Nonlinear control of a variable-speed wind turbine using a two-mass model[J]. IEEE Transactions on Energy Conversion, 26, 149-162(2011).