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Increasing use of large commercial wind turbines motives energy efficiency improvement and fatigue load mitigation in wind turbines. Advanced control methods designed with remote sensing techniques are considered as promising solutions. In this paper, we design a radial basis function neural network feedforward control based on light detection and ranging (LIDAR) measurement. In this control method, the measurements of wind-speed disturbance from LIDAR are used to train weights online in a neural network for optimizing the blade pitch angle and electromagnetic torque in a wind turbine, which is helpful in tracking the maximum wind energy and alleviate fatigue loads. The effectiveness of the proposed controller is validated with the National Renewable Energy Laboratory's typical three-blade wind turbine.

With the increasing capacity of wind turbines, the importance of the yaw system has gradually become more prominent. The supervisory control and data acquisition (SCADA) system of XEMC Windpower Co., Ltd. is used in this paper to analyze the yaw data of the No. 5 wind turbine of Baozhong Mountain Wind Farm in Hunan Province. In order to evaluate the power generation and the damage equivalent load (DEL) of the yaw bearing during the yaw process, an economic model predictive control (EMPC) yaw strategy based on light detection and ranging (LIDAR) was proposed. EMPC takes the yaw error and wind speed as the disturbance of the cost function (CF) at the same time by establishing the yaw bearing DEL look-up table in advance. Discrete adaptive yaw speed control set is proposed to adapt to different wind conditionssets. Finally, the effectiveness of EMPC is verified using the model of XE112-2000 wind turbine in simulation software Bladed.

This paper analyzes the yaw data of the 2.5MW wind turbine of XEMC Windpower Company, and the real wind information collected by the wind farm, and proposes a two-level economic model predictive control (TL-EMPC) yaw strategy based on ideal wind measurement by light detection and ranging (LiDAR). This strategy comprehensively considers the power loss caused by yaw misalignment and the structural loads of the yaw bearing during the yaw process, making the yaw system more efficient and economical: In the high wind speed range, the yaw system has a higher sensitivity by setting the threshold of the yaw error angle, so as to fully capture wind energy; in the low wind speed range, fully consider the fatigue load of the critical parts of the wind turbine during the yaw process, thus Improve the economy of the wind turbine yaw system. The fatigue load and limit load of the yaw actuator at different yaw speeds are analyzed, and the best yaw speed is obtained. The finite control set of the yaw speed is established, which is used as the constraint set of the second-level minimum objective function. Finally, an external controller was used to simulate the 2.5MW wind turbine model in Bladed, and the effectiveness of the control strategy was verified.

The structure of the modern wind turbine is becoming larger and more complex, with the wind rotor exceeding hundreds of meters in diameter. The blade shear force is also becoming increasingly serious below the rated wind speed, which leads to structure fatigue loads and instability of the generator power. For improving the dynamic performance of large wind turbines, it was proposed that individual pitch control (IPC) method was operated below the rated wind speed. In this paper, we analyze the relationship between the aerodynamic characteristics of blades and the nonlinear time-varying pitch control system based on wind shear and the tower shadow effect. The combination of IPC and torque control is used to optimize the control mode of the wind turbine. By fine-tuning the pitch angle, the unbalanced force on the wind rotor was relieved to achieve the purpose of mitigating fatigue loads. Finally, our experimental results prove the validity of the proposed IPC method below the rated wind speed by showing that it can improve power quality and reduce fatigue loads of the key components without reducing the generator output power.

The scale of China's power grid is becoming larger and larger, more and more high-voltage long-distance overhead transmission lines are exposed to the wild environment. Wildfire disasters seriously threaten the safety and stability of transmission lines. In order to monitor and warn the fire points that may affect the operation of the transmission line in advance, this research is carried out. Suomi National Polarorbiting Partnership (Suomi NPP) polar orbiting satellite and Himawari-8 geostationary satellite are used to monitor wildfires in a complementary manner, combined with improved relative threshold and adaptive dynamic threshold to identify fire points based on background radiation information. Then, a fire risk assessment system based on the entropy weight method and variable weight theory for transmission line corridors was constructed from four aspects: meteorology, underlying surface, topography and power grid. Engineering practice shows that the method proposed in this study effectively monitors and scientifically warns wildfires in the transmission line corridor, which greatly reduces the losses and working pressure caused by wildfires. INDEX TERMS Suomi NPP polar orbiting satellite, Himawari-8 geostationary satellite, improved relative threshold, adaptive dynamic threshold, entropy weight method, variable weight theory.

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