Predictive control of wind turbines by considering wind speed forecasting techniques

A wind turbine system is operated such that the points of wind rotor curve and electrical generator curve coincide. In order to obtain maximum power output of a wind turbine generator system, it is necessary to drive the wind turbine at an optimal rotor speed for a particular wind speed. A Maximum Power Point Tracking (MPPT) controller is used for this purpose. In fixed-pitch variable-speed wind turbines, wind-rotor parameters are fixed and the restoring torque of the generator needs to be adjusted to maintain optimum rotor speed at a particular wind speed for optimum power output. In turbulent wind environment, control of variable-speed fixed-pitch wind turbine systems to continuously operate at the maximum power points becomes difficult due to fluctuation of wind speeds. In this paper, wind speed forecasting techniques will be considered for predictive optimum control system of wind turbines

Maximum power point tracking for variable-speed fixed-pitch small wind turbines

Variable-speed, fixed-pitch wind turbines are required to optimize power output performance without the aerodynamic controls. A wind turbine generator system is operated such that the optimum points of wind rotor curve and electrical generator curve coincide. In order to obtain maximum power output of a wind turbine generator system, it is necessary to drive the wind turbine at an optimal rotor speed for a particular wind speed. In fixed-pitch variablespeed wind turbines, wind-rotor performance is fixed and the restoring torque of the generator needs to be adjusted to maintain optimum rotor speed at a particular wind speed for maximum aerodynamic power output. In turbulent wind environment, control of wind turbine systems to continuously operate at the maximum power points becomes difficult due to fluctuation of wind speeds. Therefore, special emphasis is given to operating at maximum aerodynamic power points of wind rotor. In this paper, the performance of a Fuzzy Logic Maximum Power Point Tracking (MPPT) controller is investigated for applications on variable-speed fixed-pitch small- scale wind turbines

Evaluation of ANN Estimation-Based MPPT Control for a DFIG Wind Turbine

This paper proposes an artificial neuronal network (ANN) estimation-based wind speed sensolress MPPT algorithm for wind turbines equipped with doubly-fed induction generators (DFIG). The ANN is designed to produce the optimal control signal for the DFIG power or speed controller. The optimal parameters of the ANN are determined by using a particle swarm optimization (PSO) algorithm. A 3.6 MW DFIG wind turbine is simulated in PSCAD to evaluate and compare the proposed MPPT method with the traditional tip speed ratio (TSR) and turbine power profile-based MPPT methods in both the speed control and power control modes in variable wind speed conditions

A Reinforcement Learning approach to the Optimal Torque MPPT problem in wind turbines

In this work, a torque controller for a variable rotational speed wind turbine has been modelled using Reinforcement Learning and considering the Optimal Torque - Maximum Power Point Tracking problem as one of optimization. The reward optimization function is designed as a non-linear function depending mainly on the rotor power variation. Based on this, an optimal action (electromagnetic torque variation) regulates the turbine rotational speed. A simulated 1.5 MW three bladed wind turbine operation is managed by the torque controller. It keeps the turbine working at optimal operational conditions after a successful training process, which is carried out using the Proximal Policy Optimization algorithm. For the controller training, the turbine confronts constant and then randomly staggered wind speed behaviour. Time series of rotor angular speed, torque and power are presented. Our results show that the modelled controller is able to reach and maintain the wind turbine operation at its optimal power generation conditions. This methodology avoids using some empirical parameter characteristic of the Optimal Torque - Maximum Power Point Tracking algorithm widely used in wind turbine control systems

Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control

This paper introduces a novel data driven yaw control algorithm synthesis method based on Reinforcement Learning (RL) for a variable pitch variable speed wind turbine. Yaw control has not been extendedly studied in the literature; in fact, most of the currently considered developments in the scope of the wind energy are oriented to the pitch and speed control. The most important drawbacks of the yaw control are the very large time constants and the strict yaw angle change rate constraints due to the high mechanical loads when the wind turbine angle is changed in order to adequate it to the wind speed orientation. An optimal yaw control algorithm needs to be designed in order to adapt the rotor orientation depending on the wind turbine dynamics and the local wind speed regime. Consequently, the biggest challenge of the yaw control algorithm is to decide the moment and the quantity of the wind turbine orientation variation to achieve the highest quantity of power at each instant, taking into account the constraints derived from the mechanical limitations of the yawing system and the mechanical loads. In this paper, a novel based algorithm based on the RL Q-Learning algorithm is introduced. The first step is to obtain a model of the power generated by the wind turbine (a real onshore wind turbine in this paper) through a power curve, that in conjunction with a conventional proportional regulator will be used to obtain a dataset that explains the actual behaviour of the real wind turbine when a variety of different yaw control commands are imposed. That knowledge is then used to learn the best control action for each different state of the wind turbine with respect to the wind direction represented by the yaw angle, storing that knowledge in a matrix Q(s,a). The last step is to model that matrix through a MultiLayer Perceptron with BackPropagation (MLP-BP) Artificial Neural Network (ANN) to avoid large matrix management and quantification problems. Once that the optimal yaw controller has been synthetized, its performance has been assessed using a number of wind speed realizations obtained using the software application TurbSim, in order to analyze how the introduced novel algorithm deals with different wind speed scenarios

School of water, energy and envitornment energy systems and thermal processes

The fast development of the wind power technology is leading to larger and more expensive wind turbines which require increasingly advance control systems to achieve optimal or near optimal operation. However, the increased optimality of complex operation schemes, like real-time optimization approaches, incur in high implementation and maintenance costs. Furthermore, while the wind speed is a key variable for the wind turbine control, using it as a direct input leads to a poor response of the power control. This makes the industry focus on simpler control structures, considering wind as a disturbance [1]. Nevertheless, baseline control laws, which perform a deterministic control, require that complex aerodynamic properties are well-known to achieve the desired performance. But in practice, variability bounds the efficiency of the energy capture. Thus, a constrained self-optimizing control is proposed to regulate the wind turbine operation coping with wind speed uncertainty. A data-driven self-optimizing control is proposed for the wind turbine control region where power is maximized (region 2). Operational data is extracted from a model off-line to examine the structure of the optimal solution. This insight is then transformed into a simple control structure capable of keeping the wind turbine to an optimal operation, in terms of maximizing power output. However, at high wind speed, wind turbine power output has to be maintained at its nominal rate. Thus, a cascade control structure for self-optimizing and constrained control is incorporated. The control structure is implemented in Simulink using as a model FAST v8 5MW onshore wind turbine model. The proposed self-optimizing control learns the structure of the optimal solution off-line and then performs the optimization strategy, adjusting both torque and pitch to maximize energy capture. This control approach leads to an increase in power output when comparing it with the deterministic baseline control. Moreover, this heuristic control approach has the potential to take into account a higher number of inputs without compromising reliability. This property allows its future application for more advance control strategies

Optimal control of wind energy conversion systems with doubly-fed induction generators

Wind energy conversion systems (WECSs) have become the interesting topic over recent years for the renewable electrical power source. They are a more environmentally friendly and sustainable resource in comparison with the fossil energy resource. The WECS using a doubly-fed induction generator (DFIG) to convert mechanical power into electrical power has a significant advantage. This WECS requires a smaller power converter in comparison with a squirrel cage induction generator. Efficiency of the DFIG-WECS can be improved by a suitable control system to maximise the output power from WECS. A maximum power point tracking (MPPT) controller such as tip-speed ratio (TSR)control and power signal feedback (PSF) control is use to maximise mechanical power from wind turbine and a model-based loss minimisation control (MBLC) is used to minimise electrical losses of the generator. However, MPPT and MBLC require the parameters of the wind turbine and the generator for generating the control laws like optimal generator speed reference and d-axis rotor current reference. The Efficiencies of the MPPT and MBLC algorithms deteriorate when wind turbine and generator parameters change from prior knowledge. The field oriented control for a DFIG in the WECS is extended by introducing a novel control layer generating online optimal generator speed reference and d-axis rotor current reference in order to maximise power produced from the WECS under wind turbine and DFIG parameter uncertainties, which is proposed. The single input rule modules (SIRMs) connected fuzzy inference model is applied to the control algorithm for optimal power control for variable-speed fixed-pitch wind turbine in the whole wind speed range by generating an online optimal speed reference to achieve optimal power under wind turbine parameter uncertainties. The proposed control combines a hybrid maximum power point tracking (MPPT) controller, a constant rotational speed controller for below-rated wind speed and a limited-power active stall regulation by rotational speed control for above-rated wind speed. The three methods are appropriately organised via the fuzzy controller based SIRMs connected fuzzy inference model to smooth transition control among the three methods. The online parameter estimation by using Kalman filter is applied to enhance model-based loss minimisation control (MBLC). The d-axis rotor current reference of the proposed MBLC can adapt to the accurate determination of the condition of minimum electrical losses of the DFIG when the parameters of the DFIG are uncertain. The proposed control algorithm has been verified by numerical simulations in Matlab/Simulink and it has been demonstrated that the energy generated for typical wind speed profiles is greater than that of a traditional control algorithm based on PSF MPPT and MBLC

Design and implementation of maximum power point tracking algorithms for wind energy conversion systems

The thesis is carried out for the wind energy conversion development project at Laboratory of Electrical Energy Engineering of Tampere University of Technology. The main purpose of the thesis is to design four different types of maximum power point tracking algorithms (MPPT) in Matlab Simulink and analyze them based on their performance for the variable speed wind energy conversion systems. Since wind speed continuously varies in wind turbine systems, the MPPT algorithms are essential to maximize the energy yield from the wind at or under rated wind speed. Variable speed wind energy system can follow the wind speed variation and generate the maximum power under the normal operating condition at lower wind speed. The efficiency of the variable speed wind turbines relies on the performance of the maximum power point tracking techniques. However, for a specific wind speed there is only one available rotor speed which is responsible for maximum power yielding known as maximum power point (MPP). In the thesis all four MPPT scheme’s theoretical backgrounds are explained and implemented in a simulation model. For all the control methods we need some input data such as wind speed, turbine characteristics, optimal power coefficient, and turbine power. In the tip speed ratio control method, precisely measured wind speed is required for the input of the MPPT controller and then the rotor speed reference is estimated from the given turbine characteristics and tip speed ratio. The simulation results show that it is the fastest control strategy to achieve the MPP but the accurate wind speed measurement is challenging. The optimal torque control and hill climb search control do not need any measurement of the wind speed but require measured turbine power. In the same manner, for the simulation of optimal torque control method, it is required to have the knowledge of optimal power coefficient which shows the optimal turbine power corresponding to the rotor speed. Eventually the most advanced method known as Modified hill climb search algorithm is simulated where turbine characteristics and wind speed measurements are not required. The thesis shows that every control method has their own pros and cons. For example, settling time for the optimal torque control and hill climb search control methods during the wind speed steps is about three times longer than with TSR control. Additionally, it is shown in simulation how the rotor speed fluctuation is eliminated with the help of Modified hill climb search algorithm. Finally, in the thesis a new concept called predictive MPPT is presented to improve the efficiency of the conventional MPPT algorithms