Top PDF Fuzzy, integer and fractional order control: application on a wind turbine benchmark model

Fuzzy, integer and fractional order control: application on a wind turbine benchmark model

Fuzzy, integer and fractional order control: application on a wind turbine benchmark model

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 wind turbine model 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 wind turbine 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 integer order proportional integral (PI) controller. A comparison between proposed control approaches for the variable speed wind turbines is presented using a wind turbine benchmark model 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- order control due to its capability to deal with system nonlinearities and uncertainties.
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Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO

Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO

This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, 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 fractional order (FO) fuzzy control 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 integer order fuzzy 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.
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Power Optimization and Control in Wind Energy Conversion Systems using Fractional Order Extremum Seeking

Power Optimization and Control in Wind Energy Conversion Systems using Fractional Order Extremum Seeking

A variable wind turbine (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 [1] where fuzzy control is used. It works only for the specified fuzzy rules. Another method based on the speed sensorless power signal feedback (PSF) [2] uses lookup table values that are dependent on the system model and parameter values. There is another method of perturb and observe (P&O) [3] is more accurate but slow convergence. To overcome these difficulties, we present fractional order 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 [4] which shows slow convergence. The convergence speed is faster in FOESC hence transient response is improved.
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Control Chaos in System with Fractional Order

Control Chaos in System with Fractional Order

However, there are essential differences between ordi- nary differential equation systems and fractional order differential systems. Most properties and conclusions of ordinary differential equation systems cannot be extend- ed to that of the fractional order differential systems. Therefore, the fractional order systems have been paid more attention. Recently, many investigations were de- voted to the chaotic dynamics and chaotic control of fractional order systems [6-12].

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Set Point Tracking of Wind Turbine Generator under Agc Using Fuzzy Logic Control

Set Point Tracking of Wind Turbine Generator under Agc Using Fuzzy Logic Control

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.
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Integer Versus Fractional Order SEIR Deterministic and Stochastic Models of Measles

Integer Versus Fractional Order SEIR Deterministic and Stochastic Models of Measles

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 fractional order version of the system of equations that was used by M . S . Bartlett [9] to model measles,

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Power Generation with the Application of Vortex Wind Turbine

Power Generation with the Application of Vortex Wind Turbine

Vortex-Bladeless is a Spanish SME whose objective is to develop a new concept of winturbine without blades called Vortex or vorticity wind turbine. 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 wind turbine 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.
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Synchronization of fractional order and integer order chaotic (hyper chaotic) systems with different dimensions

Synchronization of fractional order and integer order chaotic (hyper chaotic) systems with different dimensions

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.
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Comparison between Fuzzy Logic Control and Predictive Torque Control, applied to a DFIG based Wind Turbine

Comparison between Fuzzy Logic Control and Predictive Torque Control, applied to a DFIG based Wind Turbine

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 wind turbine. 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 wind turbine 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.
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DFIG Based Wind Turbines Behavior Improvement during Wind Variations using Fractional Order Control Systems

DFIG Based Wind Turbines Behavior Improvement during Wind Variations using Fractional Order Control Systems

This paper focuses on analyzing the output behavior of wind turbines with DFIG when using fractional-order control 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 wind turbine with PMSG using fractional-order control system can be found in [9]. The simulation results are shown in the last sections by using MATLAB/Simulink. A better operation can be seen when utilizing fractional- order control strategy.
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Advanced Control of a PMSG Wind Turbine

Advanced Control of a PMSG Wind Turbine

In this work, an intelligent artificial control of a variable speed wind turbine (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.
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Analysis of the Probability Model of Wind Load on the Offshore Wind Turbine

Analysis of the Probability Model of Wind Load on the Offshore Wind Turbine

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.

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Modelling of a PMSG Wind Turbine with Voltage Control

Modelling of a PMSG Wind Turbine with Voltage Control

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 [3] to improve the efficiency and the reliability. Inside them, MPPT integrating with the back to back space vector PWM [4] is the advantage control novel in [3], 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 wind turbine. It is integrated to adjust the aerodynamic torque of the wind turbine when this study rates wind speed.
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Conformable Fractional Order Sliding Mode Control for a Class of Fractional Order Chaotic Systems

Conformable Fractional Order Sliding Mode Control for a Class of Fractional Order Chaotic Systems

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 [26], fractional generalization of concept of stability was considered. In [27], a definition for Mittag-Leffler stability and fractional Lyapunov direct method were presented. In [28], stability analysis of FO nonlinear systems was derived using the Lyapunov direct method with Mittag-Leffler stability. In [29], 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 fractional order 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.
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Bounded solutions for fuzzy integral equations of fractional order

Bounded solutions for fuzzy integral equations of fractional order

The concept of fuzzy derivative was first introduced by Chang and Zadeh [1]. Kaleva [2], Puri and Ralescu [3] 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 [4]. 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 [9] that the boundedness of solutions of the following fuzzy integral equation:

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Speed Control of Horizontal Axis Wind Turbine

Speed Control of Horizontal Axis Wind Turbine

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
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An active fault tolerant control approach to an offshore wind turbine model

An active fault tolerant control approach to an offshore wind turbine model

For the high wind speed zone, the desired operation of the wind turbine 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.

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Advanced Issues of Wind Turbine Modelling and Control

Advanced Issues of Wind Turbine Modelling and Control

There are many aspects of wind turbine 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 [15]. 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 [16]. 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 wind turbine control [16]. 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 wind turbine 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 [16].
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Control oriented modelling of a wind turbine and farm

Control oriented modelling of a wind turbine and farm

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.
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Electrical load control of a wind turbine generator

Electrical load control of a wind turbine generator

These conditions are met by a variable speed windmill with fixed blade pitch driving a self excited induction generator, the electrical power being fed into the heating elements through [r]

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