Research On Wind Turbine Wake Dynamic Evolution Characteristics And Multi-Turbine Cooperative Optimized Control

As the international community pays more and more attention to energy security and environmental protection issues,accelerating the development of renewable energy represented by wind power has become a general consensus and concerted action of the international community to promote energy transition and respond to global climate change in recent years.At present,wind energy development is still restricted by some factors,among which the wake effect has a great influence on the wind power utilization.On the one hand,the wake effect reduces the downstream wind speed but increases the turbulence intensity,resulting in a decrease in the power generation and increase in the load conditions of the downstream wind turbines.On the other hand,under the carbon emission peak and neutrality development trend and the wind power policy subsidy cancellation,deeply excavating the influence of coupling between wind turbines caused by the wake effect and making full use of the dynamic yaw control degree of freedom is very important for the intelligent wind farm construction,which also has important theoretical significance and engineering application value.This paper takes the dynamic characteristics of the wind turbine wake as the entry point that the wake evolution dynamic process is first analyzed.Considering the order of the wake filed is usually high,it is difficult to design a controller based on high-order complex wake field.Therefore,a low-order flow estimator is first constructed,by which the wake meandering dynamic and yaw-induced wake deflection dynamic are low-order-ly approximated.On this basis,a model predictive controller is constructed,which controls the upstream wind turbines yaw angle that the downstream wake is deflected,while also realizes collaborative power generation between wind turbines.The central work in this paper includes the following parts:1.Based on high-precision CFD software,the large eddy simulation of the wind turbine wake is performed to obtain high spatial-temporal resolution wake field data.Take this on the basis,the wake dynamic characteristics are analyzed.Using the modal decomposition method,the dominant linear modes of the wake evolution process are extracted and analyzed.Furtherly,using the time-frequency domain transformation with the clustering method,the spatial distribution of the wake frequency domain characteristics is studied.The characteristics differences dues to the wake evolution in horizontal or vertical space is also analyzed.2.For a free-evolved wind turbine wake without external control inputs,we deeply integrate advantages of the dynamic mode decomposition method and linear stochastic estimation method that a Koopman-linear flow estimator is designed,which takes the probes sampling data as the measured states,and several in-direct measurement variables as deterministic states.The Koopman-linear flow estimator maintains a finite number of orders and a linear state-space form,which linearly approximates the dynamic wake field.The full-dimensional wake field is then predicted and reconstructed.3.Aiming at the control-oriented wind farm flow-field modeling,on the premise of the Koopman operator theory as the theoretical support,while also maintaining the linear state-space form and low-order requirements,a yaw-control included dynamic wake-field model is constructed,which predicts the flow field evolution process and velocity loss for a given initial condition with corresponding yaw control sequence.Combined with the Kalman filter,a closed-loop estimation structure is constructed,by which the model predicted states and the measured noise-included states are calibrated that the wake field estimation accuracy in both the time and frequency domains are improved.4.Aiming at the error accumulation situation of the low-order wake model during long-term prediction,this paper constructs the Koopman modes innovatively,which have corresponding Koopman amplitudes.This paper introduces a solving method that the optimal Koopman amplitudes is solved for a given data set in both the free-wake and yaw-controlled wake scenarios.Based on the optimized Koopman amplitudes,the Koopman modes contribution to the flow field evolution start from a specific initial condition is differently weighted.The proposed method promoting the optimal DMD amplitudes to the optimal Koopman amplitudes,while also maintaining consistency in both the free-wake and yaw-controlled wake scenarios.5.Based on the above research,an tracking error minimization optimal control problem is constructed,which takes the tracking error between power generation of multi-wind turbines and the target value as the target.The control law is solved through the receding-horizon prediction and optimization problem solution.Considering the potential industrial application possibility,the optimization problem is improved and changed that the computational requirements and time-spend during solution is heavily reduced.Using the wind turbine yaw control degree of freedom,the yaw angle of the upstream wind turbine is dynamically adjusted that its wake’s effect on downstream wind turbine is controlled and changed,so that the power generation of all wind turbines tracks the set-points.The effectiveness of the designed model predictive dynamic yaw controller is verified on a large-eddy simulation platform.