Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data

The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations

Sensor-less maximum power extraction control of a hydrostatic tidal turbine based on adaptive extreme learning machine

In this paper, a hydrostatic tidal turbine (HTT) is designed and modelled, which uses more reliable hydrostatic transmission to replace existing fixed ratio gearbox transmission. The HTT dynamic model is derived by integrating governing equations of all the components of the hydraulic machine. A nonlinear observer is proposed to predict the turbine torque and tidal speeds in real time based on extreme learning machine (ELM). A sensor-less double integral sliding mode controller is then designed for the HTT to achieve the maximum power extraction in the presence of large parametric uncertainties and nonlinearities. Simscape design experiments are conducted to verify the proposed design, model and control system, which show that the proposed control system can efficiently achieve the maximum power extraction and has much better performance than conventional control. Unlike the existing works on ELM, the weights and biases in the ELM are updated online continuously. Furthermore, the overall stability of the controlled HTT system including the ELM is proved and the selection criteria for ELM learning rates is derived. The proposed sensor-less control system has prominent advantages in robustness and accuracy, and is also easy to implement in practice

Composite hierarchical pitch angle control for a tidal turbine based on the uncertainty and disturbance estimator

With the fast development of tidal turbines for sustainable energy generations, reliable and efficient tidal pitch systems are highly demanded. This paper presents a systematic design for a novel tidal pitch system based on hydraulic servo and bevel geared transmission. This system holds the characteristics of compact and triangular structure, making it easy to be installed in a narrow turbine hub. The pitch system dynamics are modelled by taking account of model uncertainties and external disturbances. An uncertainty and disturbance estimator (UDE)-based robust pitch control algorithm is developed to achieve effective pitch angle regulation, disturbance rejection and generator power smoothing. The UDE controller is designed in a composite hierarchical manner that includes an upper level power smoothing controller and a low level pitch angle tracking controller. The performance of the proposed pitch system and the UDE control is demonstrated through extensive simulation studies based on a 600 kW tidal turbine under varying tidal speeds. Compared with the conventional controller, the UDE based pitch controller can achieve more reliable power smoothing and pitch angle tracking with higher accuracy

ADV preview based nonlinear predictive control for maximizing power generation of a tidal turbine with hydrostatic transmission

As the development of tidal turbines attracts more and more attention in recent years, reliable design and efficient control of tidal turbines are becoming increasingly important. However, the majority of existing tidal turbines still utilize traditional fixed ratio geared transmissions and the associated control designs focus on simple feedback controllers that use measurements or possibly estimates of the turbine itself or current local tidal profile. Therefore, the measurement and control are inevitably affected by the inherent delay with respect to the current tidal speeds. This paper proposes a novel tidal turbine with continuously variable speed hydrostatic transmissions and a nonlinear predictive controller that uses short-term predictions of the approaching tidal speed field to enhance the maximum tidal power generations when the tidal speed is below the rated value. The controller is designed based on an offline finite-horizon continuous time minimization of a cost function, and an integral action is incorporated into the control loop to increase the robustness against parameter variations and uncertainties. A smooth second order sliding mode observer is also designed for parameter estimations in the control loop. A 150 kW tidal turbine with hydrostatic transmission is designed and implemented. The results demonstrate that the averaged generator power increases by 6.76% with this preview based nonlinear predictive controller compared with a classical non-predictive controller

Big data driven multi-objective predictions for offshore wind farm based on machine learning algorithms

This paper explores the big data driven multi-objective predictions for offshore wind farm based on machine learning. A data-driven prediction framework is proposed to predict the wind farm power output and structural fatigue. Unlike the existing methods that are normally based on analytical models, mainly focus on single objective and ignore the control contributions, the proposed framework uses the turbine control inputs, inflow wind velocity and directions as the predictor variables. It is constructed by training five typical machine learning approaches: the general regression neural network (GRNN), random forest (RF), support vector machine (SVM), gradient boosting regression (GBR) and recurrent neural network (RNN). The assessment of these approaches is based on the FLOw Redirection and Induction in Steady State (FLORIS) under 6 different scenarios. The test results in different cases are highly consistent with each other and validate that very minor accuracy differences exist among these approaches and they all can achieve the relative accuracy of around 99% or more, which is sufficiently accurate for practical applications. The RNN and SVM exhibit the best accuracy, and particularly the RNN has the best accuracy in thrust predictions. The results also demonstrate that the GRNN has the best computational efficiency

Maximum power generation control of a hybrid wind turbine transmission system based on H∞ loop-shaping approach

The paper presents the design, modelling and optimal power generation control of a large hybrid wind turbine transmission system that seamless integrates planetary/parallel gear sets with a hydraulic transmission to improve the turbine’s reliability and efficiency. The hybrid wind turbine has power splitting flows including both mechanical and hydraulic power transmissions. The turbine transmission ratio can be controlled to continuously vary for the maximum wind power extraction and grid integration. Dynamics of the hybrid wind turbine is modeled as an incremental disturbed state space model based on the dynamic equations of each mechanical/hydraulic element. To achieve good tracking and robustness performance, an optimal H∞ loop-shaping pressure controller is designed, which accurately tracks the optimal load pressure in the hydraulic transmission for maximizing wind power generations. The validations of the proposed hybrid wind turbine and the H∞ loop-shaping pressure controller are performed based on a detailed aero-hydro-servo-elastic hybrid type wind turbine simulation platform with both mechanical geared transmission and hydraulic transmission, which is adapted from the NREL (National Renewable Energy Laboratory) 5 MW monopile wind turbine model within FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. The validation results demonstrate that the hybrid wind turbine achieves better performance in both the maximum wind power extraction and power quality than the hydrostatic wind turbine. In addition, the proposed H∞ loop-shaping pressure controller has better tracking performance than the traditional proportional integral (PI) controller

Optimal power extraction of a two-stage tidal turbine system based on backstepping disturbance rejection control

This paper investigates the optimal power generation control for a two-stage horizontal-axis tidal turbine system based on backstepping disturbance rejection control (BDRC), which is a new control framework for high-order nonlinear systems. The tidal turbine system is designed with the main structure being described. The dynamics of the tidal turbine system is then formulated based on the integration of the dynamics of its constitute components. The tidal turbine experiences large uncertainties and unknown dynamics from non-uniform operating thrust and fatigue forces, variations and turbulence in tidal flow velocities induced by waves and wind. The proposed BDRC is a unique control concept which has superior performance in dealing with these large uncertainties without requiring much information about the turbine dynamics. Consequently, the BDRC system is synthesized in two control loops for the control of the turbine dynamics (outer loop) and the q-axis current dynamics (inner loop) respectively to track the optimal tidal turbine speed and hence maintain the optimal power generations. Both the stability and convergence of the two closed control loops are subsequently analyzed and proved. The simulations are conducted in MATLAB/Simulink to compare the performance of the developed BDRC system with a sliding mode control method used in the literature. In addition, the proposed BDRC algorithm is extended to a general nonlinear strict-feedback system with high uncertainties and external/internal disturbances. The proposed BDRC has significantly extended the traditional ADRC and does not need any differential operations, thereby totally eliminating the inherent problems of “explosion of complexity” and repeated differentiations of virtual control variables in traditional backstepping control

Data driven learning model predictive control of offshore wind farms

This paper presents a data-driven control approach for maximizing the total power generation of the offshore wind farm by using a recently developed learning model predictive control (LMPC) algorithm. The control is designed by coordinating yaw angle control actions of wind turbines to mitigate the wake interactions among the turbines for increasing the total farm power production, which is termed as wake redirection. This paper mainly focuses on designing the architecture and methodology of the LMPC for wind farm, including a unified wind turbine wake interaction model, the LMPC for minimizing an iteration cost function, the recursive feasibility, stability and convergence analysis. Extensive comparative studies are conducted to verify the performance of the LMPC in comparison with the existing model predictive control (MPC) method under the same wind speed conditions. The results show that the wind farm yields up to 15% more power production by using the LMPC than the conventional MPC

Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations

In this paper, a reliability aware multi-objective predictive control strategy for wind farm based on machine learning and heuristic optimizations is proposed. A wind farm model with wake interactions and the actuator health informed wind farm reliability model are constructed. The wind farm model is then represented by training a relevance vector machine (RVM), with lower computational cost and higher efficiency. Then, based on the RVM model, a reliability aware multi-objective predictive control approach for the wind farm is readily designed and implemented by using five typical state of the art meta-heuristic evolutionary algorithms including the third evolution step of generalized differential evolution (GDE3), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective particle swarm optimization (MOPSO), the multi-objective grasshopper optimization algorithm (MOGOA), and the non-dominated sorting genetic algorithm III (NSGA-III). The computational experimental results using the FLOw Redirection and Induction in Steady-state (FLORIS) and under different inflow wind speeds and directions demonstrate that the relative accuracy of the RVM model is more than 97%, and that the proposed control algorithm can largely reduce thrust loads (by around 20% on average) and improve the wind farm reliability while maintaining similar level of power production in comparison with a conventional predictive control approach. In addition, the proposed control method allows a trade-off between these objectives and its computational load can be properly reduced