Abstract — This paper presents a comparison between proportional integral control approaches for variable speed wind turbines. Integer and fractional-order controllers are designed using linearized windturbinemodel whilst fuzzy controller also takes into account system nonlinearities. These controllers operate in the full load region and the main objective is to extract maximum power from the windturbine while ensuring the performance and reliability required to be integrated into an electric grid. The main contribution focuses on the use of fractional-order proportional integral (FOPI) controller which benefits from the introduction of one more tuning parameter, the integral fractional-order, taking advantage over integerorder proportional integral (PI) controller. A comparison between proposed control approaches for the variable speed wind turbines is presented using a windturbinebenchmarkmodel in the Matlab/Simulink environment. Results show that FOPI has improved system performance when compared with classical PI and fuzzy PI controller outperforms the integer and fractional- ordercontrol due to its capability to deal with system nonlinearities and uncertainties.
This paper investigates the operation of a hybrid power system through a novel fuzzycontrol scheme. The hybrid power system employs various autonomous generation systems like windturbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractionalorder (FO) fuzzycontrol scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integerorderfuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.
A variable windturbine (WT) generates power in subrated and rated power regions. In the subrated region Fig.2. region II , the maximum achievable turbine power is a function of the turbine speed at any velocity. The maximum power tracking algorithms developed in the recent are model dependent. They have the drawback that the controller needs to be redesigned for each wecs. A similar system is presented in  where fuzzycontrol is used. It works only for the specified fuzzy rules. Another method based on the speed sensorless power signal feedback (PSF)  uses lookup table values that are dependent on the system model and parameter values. There is another method of perturb and observe (P&O)  is more accurate but slow convergence. To overcome these difficulties, we present fractionalorder extremum seeking (FOES) algorithm which is non model based and with easily tunable design parameters. The FOESC has many advantages over ESC.A system with ESC is shown in  which shows slow convergence. The convergence speed is faster in FOESC hence transient response is improved.
However, there are essential differences between ordi- nary differential equation systems and fractionalorder differential systems. Most properties and conclusions of ordinary differential equation systems cannot be extend- ed to that of the fractionalorder differential systems. Therefore, the fractionalorder systems have been paid more attention. Recently, many investigations were de- voted to the chaotic dynamics and chaotic control of fractionalorder systems [6-12].
This paper explains about the different types of set-point controls for variable-speed wind generators. Both pitch angle control (PAC) and Rotor speed control (RSC) having their own advantages and disadvantages. Compared to PAC, RSC have better inertial effect which leads to better tracking in set-point command. Below set-point management this proposed paper illustrates the controllability of wind kind. However, the sturdiness of inertia impact mainly depends on the allowable rotor speed vary. The projected wind farm models not only ready to portray the set-point management underneath variable wind speed, this gives best solution by reducing the calculation burden which is useful for system analysis. Here we are using the fuzzy controller compared to other controllers. The different characteristics of wind farm and performance are shown in simulation results.
E (α) ( t ) : = N 1 E ( X 2 (α) ( t )) , and I (α) ( t ) : = N 1 E ( X 3 (α) ( t )) , where N is the total population size and E ( x ) is the expected value of x. Thus using equation (11), and approximating E ( X 1 (α) ( t ) X (α) 3 ( t )) by E ( X 1 (α) ( t )) E ( X 3 (α) ( t )) we reach the fractionalorder version of the system of equations that was used by M . S . Bartlett  to model measles,
Vortex-Bladeless is a Spanish SME whose objective is to develop a new concept of winturbine without blades called Vortex or vorticity windturbine. This design represents a new paradigm in wind energy and aims to eliminate or reduce many of the existing problems in conventional generators. Due to the significant difference in the project concept, its scope is different from conventional wind turbines. It is particularly suitable for offshore configuration and it could be exploited in wind farms and in environments usually closed to existing ones due to the presence of high intensity winds. The device is composed of a single structural component, and given its morphological simplicity, its manufacturing, transport, storage and installation has advantages. The new windturbine design has no bearings, gears, etcetera, so the maintenance requirements could be drastically reduced and their lifespan is expected to be higher than traditional turbines. It is clear that the proposed device is of prime interest, and that scientific investigation of the response of this wind energy generator under different operation scenarios is highly desirable. Thus, the objective of this SHAPE project is to develop the needed tools to simulate Fluid-Structure Interaction (FSI) problems and to reproduce the experimental results for scaled models of the Vortex- Bladeless device. In order to do so the Alya code, developed at the Barcelona Supercomputing Center, is adapted to perform the Fluid-Structure Interaction (FSI) problem simulation.
It should be pointed out that in the above mentioned literature, synchronization of fractional-order or integer-order chaotic systems was mainly discussed. Synchronization between fractional-order and integer-order chaotic systems is widely perceived as con- tributing to generating hybrid chaotic transient signals, which are quite diﬃcult to be de- crypted in communication. Up to now, only a few works have been given to investigate this problem, for instance, by using the stability theory of fractional-order linear system, Ref.  investigated modiﬁed general functional projective synchronization between a class of integer-order and fractional-order chaotic systems. Ref.  discussed the dual projective synchronization between integer-order and fractional-order chaotic systems (one can refer to [–] for more details). Actually, some dynamical systems usually have non-identical dimensions. However, papers which have discussed the synchroniza- tion between fractional-order and integer-order chaotic (hyper-chaotic) systems with dif- ferent dimensions are not common. Ref.  investigated adaptive generalized function matrix projective lag synchronization between fractional-order and integer-order com- plex networks with delayed coupling and diﬀerent dimensions. However, the controller in  has a very complicated form. Note that two scaling matrices (a function matrix and a non-unit constant matrix), which are more general than other scaling factors in FPS, have not been used to discuss the synchronization between fractional-order and integer-order chaotic (hyper-chaotic) systems with diﬀerent dimensions. Besides, it is well known that the quadratic Lyapunov functions provide an important tool for stability analysis in the integer-order nonlinear systems. Therefore, how to use quadratic Lyapunov functions in the stability analysis of fractional-order systems is meaningful.
Abstract: In this paper, Fuzzy Logic Control (FLC) and Predictive Torque Control (PTC) methods are being introduced separately at grid side converter (GSC) of Doubly Fed Induction Generator (DFIG) based windturbine. There are two parameters usually challenging for the power rating of a wind energy conversion system (WECS): improper control strategy for RSC, which is the primary reason behind current harmonics and for GSC, it is the reason behind voltage harmonics. In several windturbine systems while maintaining these harmonics leads to improper electromagnetic torque. This two fold effect of controller is worst while using same controller for both GSC and RSC. Firstly, in this improved controller architecture, the general structure of conventional controller is retained and Fuzzy Logic is used to provide intelligence to the operation of controller. In several research works it has been seen different controllers for both side, resulting improved results. Secondly, a predictive methodology has been adopted which predicts torque and stator current to be used as reference signal in controlling GSC and a well-tuned PI controller for RSC. The results are being compared that shows significant reduction in harmonics at rotor side and grid side.
This paper focuses on analyzing the output behavior of wind turbines with DFIG when using fractional-ordercontrol system and improves the power quality and power and voltage stability in comparison with using classic control system during wind variations, considering two types of ac/dc/ac power electronic converters. Also, modeling, control and simulation of windturbine with PMSG using fractional-ordercontrol system can be found in . The simulation results are shown in the last sections by using MATLAB/Simulink. A better operation can be seen when utilizing fractional- ordercontrol strategy.
In this work, an intelligent artificial control of a variable speed windturbine (PMSG) is proposed. First, a mathematical model of turbine written at variable speed is established to investigate si- mulations results. In order to optimize energy production from wind, a pitch angle and DC bus control law is synthesized using PI controllers. Then, an intelligent artificial control such as fuzzy logic and artificial neural network control is applied. Its simulated performances are then com- pared to those of a classical PI controller. Results obtained in MATLAB/Simulink environment show that the fuzzy and the neuro control is more robust and has superior dynamic performance and hence is found to be a suitable replacement of the conventional PI controller for the high per- formance drive applications.
The characteristic of wind velocity is fluctuant. The value of wind velocity at a fixed height fluctuates around a certain value, which is called static wind velocity. The random fluctuation value of wind velocity is called fluctuating wind velocity. Take horizontal component of wind velocity (horizontal wind velocity) as an example, its formula is shown in as follows.
speed, does not have rotor current, and can be used without gearbox. The high efficiency and low maintenance will reduce the cost that is the most concern to invest. However, PMSG still has some drawbacks. It needs electromagnetic field with the flexible structure, which leads to the high standard of the production as well as of the operation. Furthermore, variable speed of the generator has to be known by power inverter too. According to the continuous development of wind power technology, the efficiency of inverter device, facing some tough issues, plays an important role in the improvement of wind power generation system performance. They need to be enhanced by novel controller  to improve the efficiency and the reliability. Inside them, MPPT integrating with the back to back space vector PWM  is the advantage control novel in , which is used to measure the rotor speed and compare with the calculated optimal rotor speed. On the other hand, not only does the inverter take an advantage in efficiency control but also the pitch angle controller takes another important part of windturbine. It is integrated to adjust the aerodynamic torque of the windturbine when this study rates wind speed.
Stability analysis of nonlinear systems is necessary to design a controller. Several works have investigated stability analysis of nonlinear systems by means of fractional calculus [26-30]. In , fractional generalization of concept of stability was considered. In , a definition for Mittag-Leffler stability and fractional Lyapunov direct method were presented. In , stability analysis of FO nonlinear systems was derived using the Lyapunov direct method with Mittag-Leffler stability. In , stability of fractional differential systems based on the conformable fractional derivatives was studied. However, there are very few papers considering modelling of the nonlinear systems with conformal FO definition [29,30]. Therefore, application of the conformable FO operators in the design of FO controller is an open area. Accordingly, for the first time, in this paper, a FO sliding mode control is designed for a class of conformable fractionalorder chaotic system using the conformable fractional derivative and the superiority of the proposed controller is shown. Having these facts in mind, the main contributions of this paper in comparison with previous researches are as follows. A novel FO manifold using conformable FO operators is proposed to control chaotic systems in the presence of uncertainties and disturbances. The conformable FO operator as an interesting definition is applied in designing of the FO sliding mode controller. Based on conformable FO operators, the stability of the controller is derived using the Lyapunov direct method. The main advantage of the proposed control method is fast convergence speed with together less chattering and complexity in calculations.
The concept of fuzzy derivative was ﬁrst introduced by Chang and Zadeh . Kaleva , Puri and Ralescu  introduced the notion of fuzzy derivative as an extension of the Hukuhara derivative and the fuzzy integral, which was the same as that proposed by Dubois and Prade . There has been a signiﬁcant development in the study of fuzzy dif- ferential and integral equations (see, for example, [5–8], and the references therein). Under suitable conditions, it was proved in  that the boundedness of solutions of the following fuzzy integral equation:
In recent years renewable sources of power are being increasingly exploited to address the challenges of climate change and fossil fuel depletion. Wind power is one of the few renewable energy sources capable of rapidly satisfying a reasonable proportion of future energy requirements. Wind turbines will be expected to significantly increase the power outputs with an improved efficiency and reliability. The advantages of wind energy are more apparent than the disadvantages. Next, harvesting wind power is a clean, non-polluting way to generate electricity. Unlike other types of power plants, it emits no air pollutants or greenhouse gases. The wind turbines harmlessly generate electricity from wind passing by. Since the population of India keeps on increasing, the implementation of wind turbines with most profitable power production is needed to satisfy the future power needs. Wind energy, with an average growth rate of 30%, is the fastest growing source o f r e n e w a b l e energy in the world. India occupies the fifth place in the w o r l d i n wind energy generation after USA, Germany, Spain, and China. Since t h e maximum speed of wind blade is from 55 mph to 161 mph, it is necessary to
For the high wind speed zone, the desired operation of the windturbine is to keep the rotor speed and the generator power at constant values. The main idea is to use the pitch system to control the efficiency of the aerodynamics while applying the rated generator torque. However, in order to improve tracking of the power reference and cancel steady state errors on the output power, a power controller is usually considered as well [3, 8, 9]. Hence, both speed control and power control are included in practice.
There are many aspects of windturbine performance that can be improved with more advanced control development. Researchers have developed methods for using adaptive control to compensate for unknown or time–varying parameters . Other researchers have also begun to investigate the addition of feed-forward control to improve the disturbance rejection performance when the incoming wind profile deviates from that expected . Most of these feed-forward controllers use estimates of the disturbance (or wind deviation). New sensing technologies will enable various avenues of advanced control research. For instance, there has been recent interest in evaluating the potential of LIDAR (which stands for “LIght Detection And Ranging”) sensors for windturbinecontrol . LIDAR is a remote optical sensing technology that has been used since the 1970s for meteorology for measuring wind speed profiles for monitoring hurricanes and wind conditions around airports. New lidar systems based on solid–state sources and off–the–shelf telecommunications equipment allow for inexpensive deployment, modularity, and improved reliability. Depending on the particular type of technology used, lidar sensors can provide quantities representing the wind speed and direction and various wind turbulence and shear parameters. An accurate measurement of the wind profile over the entire rotor plane in Fig. 7 can enable feed-forward pitch control and feed-forward torque control to improve performance dramatically. Advanced windturbine controllers are further discussed and compared in [4, 16]. As turbines get larger and blades get longer, it is possible that turbine manufacturers will build turbines that allow for different pitch angles at different radial positions along the blades relative to the standard blade twist angle. In this case, separate actuators and controllers may be necessary, opening up even more control opportunities .
The continuous model reported in Section 2 includes various modules and dynamics, including aerodynamics module, rotor dynamics module and drive-train module. The drive-train module is stiff and linear. Stiff systems have poles of very different magnitudes, i.e. some poles close to the origin (in the s-plane) and some poles very far away from them, meaning that they contain both slow and fast dynamics. This therefore makes it difficult for the numerical integration solver of Matlab/SIMULNIK to choose a correct and optimal sampling step when simulating the stiff drive-train module, and the solver thus has a strong tendency to opt for a very small sampling step, as opposed to a larger optimal sampling step, resulting in a significant increase in simulation time. This is avoided here by optimally discretising the stiff drive-train module in advance and thereby not requiring numerical integration to be carried out at all during simulation.