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

Integrated Optimal Design of a Passive Wind Turbine System: An Experimental Validation

This work presents design and experimentation of a full passive wind turbine system without active electronic part(power and control). The efficiency of such device can be obtained only if the system design parameters are mutually adapted through an Integrated Optimal Design (IOD) method. This approach based on multiobjective optimization, aims at concurrently optimizing the wind power extraction and the global system losses for a given wind speed profile while reducing the weight of the wind turbine generator. It allows us to obtain the main characteristics (geometric and energetic features) of the optimal Permanent Magnet Synchronous Generator (PMSG) for the passive wind turbine. Finally, experiments on the PMSG prototype built from this work show a good agreement with theoretical predictions. This validates the design approach and confirms the effectiveness of such passive device

Bayesian reinforcement learning with MCMC to maximize energy output of vertical axis wind turbine

Optimization of energy output of small scale wind turbines requires a controller which keeps the wind speed to rotor tip speed ratio at the optimum value. An analytic solution can be obtained if the dynamic model of the complete system is known and wind speed can be anticipated. However, not only aging but also errors in modeling and wind speed prediction prevent a straightforward solution. This thesis proposes to apply a reinforcement learning approach designed to optimize dynamic systems with continuous state and action spaces, to the energy output optimization of Vertical Axis Wind Turbines (VAWT). The dynamic modeling and load control of the wind turbine are accomplished in the same process. The proposed algorithm is a model-free Bayesian Reinforcement Learning using Markov Chain Monte Carlo method (MCMC) to obtain the parameters of an optimal policy. The proposed method learns wind speed pro les and system model, therefore, can utilize all system states and observed wind speed pro les to calculate an optimal control signal by using a Radial Basis Function Neural Network (RBFNN). The proposed method is validated by performing simulation studies on a permanent magnet synchronous generator-based VAWT Simulink model to compare with the classical Maximum Power Point Tracking (MPPT). The results show signi cant improvement over the classical method, especially during the wind speed transients, promising a superior energy output in turbulent settings; which coincide with the expected application areas of VAWT

ANN-Based Adaptive PI Control for Wind Turbine with Doubly Fed Induction Generator

This paper focuses on developing a novel algorithm which dynamically optimizes the controllers of doubly fed induction generator (DFIG) driven by a wind turbine (WT) to increase DFIG transient performance in all wind speed conditions. Particle swarm optimization (PSO) is proposed to optimize parameters of PI controllers of DFIG’s rotor side/grid side converters (RSC/GSC) at different wind speeds in order to maximize the damping ratios of the system eigenvalues in small signal stability analysis. Based on the optimal values and the wind speed data set, an artificial neural network (ANN) is designed, trained, and it has the ability to quickly forecast the optimal values of parameters. Adaptive PI controllers (including ANN) are designed which dynamically change PI gain values according to different wind speeds. Simulation is done via PSCAD software for a single machine connected to an infinite bus (SMIB) system. The results show that the DFIG of ANN based adaptive PI control could significantly contribute in the transient performance improvement in a wide wind speed range

MULTIDIMENSIONAL OPTIMAL DROOP CONTROL FOR WIND RESOURCES IN DC MICROGRIDS

Two important and upcoming technologies, microgrids and electricity generation from wind resources, are increasingly being combined. Various control strategies can be implemented, and droop control provides a simple option without requiring communication between microgrid components. Eliminating the single source of potential failure around the communication system is especially important in remote, islanded microgrids, which are considered in this work. However, traditional droop control does not allow the microgrid to utilize much of the power available from the wind. This dissertation presents a novel droop control strategy, which implements a droop surface in higher dimension than the traditional strategy. The droop control relationship then depends on two variables: the dc microgrid bus voltage, and the wind speed at the current time. An approach for optimizing this droop control surface in order to meet a given objective, for example utilizing all of the power available from a wind resource, is proposed and demonstrated. Various cases are used to test the proposed optimal high dimension droop control method, and demonstrate its function. First, the use of linear multidimensional droop control without optimization is demonstrated through simulation. Next, an optimal high dimension droop control surface is implemented with a simple dc microgrid containing two sources and one load. Various cases for changing load and wind speed are investigated using simulation and hardware-in-the-loop techniques. Optimal multidimensional droop control is demonstrated with a wind resource in a full dc microgrid example, containing an energy storage device as well as multiple sources and loads. Finally, the optimal high dimension droop control method is applied with a solar resource, and using a load model developed for a military patrol base application. The operation of the proposed control is again investigated using simulation and hardware-in-the-loop techniques

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

Developing an effective route for education in wind renewable energy

This research develops an effective route for education in wind renewable energy. A resourceful educational method is developed by using a wind simulated system. Wind turbines convert the power from wind to electricity. As electricity is generated, the wind turbines are linked to selected electrical networks. The most common design of wind turbine is the horizontal axis wind turbine (HAWT). HAWT rotors are usually classified according to the rotor orientation, hub design, rotor control, number of blades and how they are aligned with the wind. The rotor contains of the hub and blades of wind turbine. They are the turbine’s most important mechanisms from both performance and overall cost standpoint. Wind turbine blades must be designed to transform the kinetic energy into torque. Wind speed is an important factor in wind system performance. Therefore, in order to obtain optimal performance, the location and elevation of the turbines is crucial. Due to the inability of having a real wind turbine in preferable locations, alternative methods are being established. This research project proposes the Lucas-Nuelle wind simulator as a viable solution to these constraints. The Lucas-Nuelle training system uses a modular design. It generates power by using a small wind energy generator that simulates the rotation of the shaft in the wind turbine as the wind speed increases. It then records the amount of power, voltage, and current at different speeds. The Lucas-Nuelle system is a valuable substitution of an actual wind turbine. As a result, the wind simulator system imitates how wind turbines work and perform over changes of wind speed, pitch and elevation

Low-Cost (PM Less) Wide-Speed-Range-Operation Generators

This chapter presents a novel dual stator-winding induction generator (DSWIG) system for wind power generation, and an optimal scheme to decrease the capacity of static excitation converter (SEC) is also given. The main result is represented by the finding that reactive excitation power released by the excitation capacitor and SEC is not only correlated to generator parameters, speed range, and load but also affected by wind turbine power curve. This chapter also investigates the optimal excitation capacitor selection process. Considering the objective of minimizing the capacity of SEC, several methods are tested here to identify an appropriate excitation capacitor value. Using the general d-q model in the stator-voltage-orientation synchronous frame of the DSWIG control algorithm and model of SEC, a decoupling control strategy using the space vector modulation (SVM) is determined for the six-phase DSWIG. Based on the obtained models, the computer simulation and experimental investigations of a test prototype orated at 18 kW with six stator phases and three-phase wound rotor DSWIG wind power system were carried out to validate the optimal solution for the system The matching results (simulation and teststand measurements) demonstrate the correctness and effectiveness of this optimization scheme