CHAPTER 4 WIND TURBINE MODELING AND SRG BASED WIND ENERGY CONVERSION SYSTEM

Transcription

1 85 CHAPTER 4 WIND TURBINE MODELING AND SRG BASED WIND ENERGY CONVERSION SYSTEM 4.1 INTRODUCTION Wind energy is one of the fastest growing renewable energies in the world. The generation of wind power is clean and non-polluting; it does not produce any harmful by-products to the environment. A Wind Turbine (WT) is a machine for converting the kinetic energy of the wind into mechanical energy. If the mechanical energy is used directly by machinery, such as a pump or grinding stones, the machine is called a windmill. If the mechanical energy is then converted to electricity, the machine is called a wind generator. Wind turbines that operate at a constant speed attain the greatest aerodynamic efficiency levels only when the wind speed is the same as the designed wind speed. In variable speed wind power systems, the turbine runs at a tip speed ratio which ensures its maximum efficiency. Variable speed systems have many advantages such as that the turbine is less sensitive to the wind pattern of a given location and emits less noise at low speed, (Rajveer Mittal et al 2010) Turbine Classification There are two different types of wind energy conversion devices: those which depend mainly on aerodynamic lift and those which use mainly aerodynamic drag. High speed turbines rely on lift forces to move the blades.

2 86 To generate electricity from a wind turbine, it is usually desirable that the driving shaft of the generator operates at considerable speed (1500 revolutions per minute). Wind turbines can also be classified based by the axis in which the turbine rotates. Turbines that rotate around a horizontal axis are more common and said to be Horizontal-Axis Wind Turbine (HAWT). Turbines with vertical axis are called as Vertical-Axis Wind Turbines (VAWT), which are less frequently used. Therefore, HAWT is selected for SRG based wind electric generation and it is shown in Figure 4.1. Figure 4.1 Horizontal axis wind turbine Modern Wind Turbines Turbines used in wind farms for commercial production of electric power are usually three-bladed and pointed into the wind by computercontrolled motors. These have high tip speeds of up to six times the wind speed, high efficiency, and low torque ripple, which contribute to good reliability. The blades usually have range in length from 20 to 40 metres

3 87 (65 to 130 ft) or more. The tubular steel towers range from 200 to 300 feet (60 to 90 metres) tall. The blades rotate at revolutions per minute. A gear box is commonly used to step up the speed of the turbine, although designs may also use direct drive of an annular generator. Some models operate at constant speed, but more energy can be collected by variable-speed turbines which use a solid-state power converter to interface to the transmission system. All turbines are equipped with control systems. These systems employ anemometers and wind vanes to determine wind speed and direction. Based on this information, the turbine yaw drive will turn the blade face into the wind, and the blade pitch can be altered to maximize the power output. The control system will shut the turbine down to avoid the damage when the wind speed exceeds the cut-off value. 4.2 WIND TURBINE MODELING Modeling is a basic tool for analysis, such as optimization, project, design and control. Wind energy conversion systems are very different in nature from the conventional generators, and therefore dynamic studies must be addressed in order to integrate wind power into the power system. Models utilised for steady-state analysis are extremely simple, while the dynamic models for wind energy conversion systems are not easy to develop. Dynamic modeling is needed for various types of analysis related to system dynamics: stability, control system and optimization, (Jasmin Martinez 2007). A typical wind energy conversion system is displayed in Figure 4.2. Figure 4.2 Wind turbine scheme

4 88 Wind Turbine (WT) is a machine for converting the kinetic energy in the wind into mechanical energy. The kinetic energy in a flow of air through a unit area perpendicular to the wind direction, per mass flow rate is computed as 1 E= v 2 (4.1) 2 where v wind speed (m/sec) For an air stream flowing through an area A the mass flow rate is Av and therefore the power in the wind is written as 1 P 3 w = rav (watts or J/sec) (4.2) 2 where air density=1.225 Kg/m 3 A area covered by blades, m 2 From equation (4.2), it is clear that the power available from the wind is a function of the cube of the wind speed. It means that a doubling of the wind speed gives eight times the power output from the turbine. Therefore, turbines have to be designed to support higher wind loads than those from which they can generate electricity, to prevent them from damage. Wind turbines reach the highest efficiency at a wind speed between 10 and 15m/s. Above this wind speed, the power output of the rotor must be controlled to reduce the driving forces on the rotor blades as well as the load on the whole wind turbine structure, (Ackermann et al 2000). The co-efficient of performance (C p ) is defined as the fraction of energy extracted by the wind turbine of the total energy that would have flowed through the area swept by the rotor if the turbine had not been there.

5 89 C p has a maximum theoretical value of The maximum theoretical C p was first formulated in 1919 by Betz and it is applicable to all types of wind turbines. The expression for co-efficient of performance (C p ) is described as P C (, ) extracted p lb = (4.3) P w where P extracted Power extracted by the wind turbine and it is expressed as P 1 = C (, ) Av 3 extracted p l b r (4.4) 2 where pitch angle, degrees tip speed ratio The ratio of speed of the tip of the blade to the wind velocity, referred to as the tip speed ratio ( ) and it is written as wr l = (4.5) v Where rotor speed, mechanical radian/sec R rotor radius (blade length), m The rotor torque T w is given as 1 (, ) 3 P Cp l b rav T extracted 2 w = w = w (4.6) The area covered by the blades is given by A= pr 2 (4.7)

6 90 Substituting equation (4.7) into equation (4.6) leads to 1 p (, ) 23 2 C p lbr R v T w = (4.8) w Maximum Power Point Tracking The maximum extractable power from a renewable energy source depends not only on the strength of the source but also on the operating point of the energy conversion system. Therefore, the MPPT is of paramount importance in the renewable energy conversion systems for not only to maximize the system efficiency but also to minimize the return period of the installation cost, (Syed Muhammad Raza Kazmi et al 2010). In a Wind Energy Conversion System (WECS), the concept of MPPT is to optimize the generator speed relative to the wind speed intercepted by the wind turbine such that the power is maximized. There are different techniques on MPPT in WECS. They are look up table method state space method the neural network-fuzzy logic method the hill climbing method The most widely used MPPT technique is lookup table method. This method requires either a pre-programmed 2D lookup table with stored values of optimal generator speed and the corresponding maximum power (maximum torque) at various wind velocities; or a cubic (quadratic) mapping function to provide reference signal for optimal turbine power (torque) at the

7 91 operating generator speed. Almost all the lookup table based MPPT methods have the stored optimal values which are constants. There are a number of factors that can cause drift in these values and therefore the stored curve or mapping function may not remain valid for the optimal power extraction. Being a nonlinear control problem, linear and nonlinear state space control theory have found their applications in the control of WECS. For a specific well defined wind turbine and generator, these state space techniques may provide robustness against disturbances. However, the formulation of these techniques require system modeling which may not be available or known in general. Therefore, these techniques are difficult to implement and their system specific nature makes them sensitive to the drift or modifications in the system parameters. Artificial Neural Network (ANN), fuzzy logic and neuro-fuzzy control techniques are also used towards the maximum power extraction control of wind energy systems. ANN based control can be quite effective and robust only after it is sufficiently trained for all kinds of operating conditions. This is quite a tough requirement and requires long offline training. This long offline training makes ANN quite unattractive for the real time practical applications. The ANN for its training requires wind velocity sensor additionally with the generator speed sensor which is again not a good feature. Fuzzy control requires defining lots of boundaries and gains. Defining optimal set of rules and corresponding control actions are very difficult and there are no proper general purpose guidelines for the selection and optimization. The hill shaped power curves of WECS exhibit unique maxima with respect to its control variable (generator speed or converter duty ratio). Hence, a simple discrete time Hill Climb Searching (HCS) control can be

8 92 employed by perturbing the control variable and observing the increase or decrease in power; hence also known as Perturb & Observe (P&O) technique. If it results in the increase in power then the same perturbation is applied for the next control instance; otherwise the sign of the perturbation is reversed in order to track the direction of increasing power. In this research work, HCS control is used to obtain maximum power. HCS or P&O technique is the simplest MPPT algorithm that does not require any prior knowledge of the system or any additional sensor except the measurement of the power which is subjected to maximization. Therefore HCS can be applied to any renewable energy conversion system that exhibits a unique power maximum. The Principle of HCS MPPT is to keep the perturbing control variable in the same direction until the power is decreased. Its Control law is illustrated in Figure 4.3. Figure 4.3 Principle of HCS

9 MPPT Algorithm Step 1: Initialize power equal to zero and define all the variables. Step 2: Compute power for the given speed. Step 3: Find out error between calculated power and initial power. Step 4: If the error is greater than zero then assign initial power as calculated power and increase the speed. Step 5: If the error is less than zero then the initial power is not changed. Step 6: Finally initial power is equal to maximum power and plot the wind turbine characteristics. Step 7: From the wind turbine characteristics, speed corresponding to maximum power is identified to extract maximum power from the wind turbine Flowchart in Figure 4.4. The flow chart representing the HCS algorithm is given

10 94 START Initialize P i =0 n=0 for speed=1:x NO YES Compute Power P(speed) Error= P(speed)-P i If Error>0 NO YES P i =P(speed) n=n+1 P i =P i maxp=p i display(maxp) display(n) END STOP Figure 4.4 Flow chart for HCS

11 95 Coding is developed in MATLAB to obtain the response of WT with MPPT capability. The wind turbine characteristic for variable speed operation with optimal line is shown in Figure 4.5. The WT is modeled, considering the cut-in velocity of 4 m/s and the rated velocity of 12 m/s. Figure 4.5 Wind turbine characteristics The optimal line is drawn by joining maximum power points corresponding to different wind velocities. Equation of the optimal line is obtained using curve fitting technique in the MATLAB and it is expressed as P (opt) = 1.6e -05 x 3 2.4e -17 x e -14 x 1.3e -12 (4.9) where x is speed in RPM From equation (4.9) the optimum power for different rotor speed is obtained and conduction angle of switches in the converter is adjusted to get optimum power.

12 MODELLING OF THE DRIVE TRAIN The mechanical parts of a wind turbine system in general consist of a blade pitching mechanism, a hub with blades, a rotor shaft and a gearbox with generator. The drive train includes the inertia of both the turbine and the generator. The moment of inertia of the wind wheel is about 90% of the total moment of the drive train, while the generator rotor moment of inertia is equal to about 10%. However, the generator represents the biggest torsional stiffness. The common way to model the drive train of a wind turbine in power system operation analysis is based on the assumption of two lumped/masses only: the generator (with gearbox) mass and the hub with blades (wind wheel) mass. The structure of the model is presented in Figure 4.6. Figure 4.6 Drive train dynamics

13 97 The equation of motion of the generator is given by Hg dw g T = T m dt e + (4.10) n where H g - inertia constant of the generator g T e T m n - rotor speed of the generator - electromagnetic torque - mechanical torque - gear ratio Since the wind turbine shaft and generator are coupled together via a gearbox, the wind turbine shaft system should not be considered stiff. To account for the interaction between the windmill and the rotor, an additional equation describing the motion of the windmill shaft is expressed as dw H m m = Tw - T dt m (4.11) where H m - inertia constant of the turbine m T w - rotor speed of the turbine - torque provided by the wind The mechanical torque T m can be modelled with the following equation q wg -wm Tm = K + D (4.12) n n dq = w dt g- wm (4.13)

14 98 Where - angle between the turbine rotor and the generator rotor K D - stiffness constant - damping constant 4.4 SRG BASED WIND ENERGY CONVERSION SYSTEM When SRG is intended for wind energy applications, the focus is to maximize the power generation as much as possible during any speed range. Therefore, model of wind turbine is developed by incorporating MPPT algorithm. The torque contributed by the wind turbine is coupled with SRG system through the gear arrangement to obtain SRG based wind energy conversion system. The MATLAB/Simulink model of SRG based wind energy conversion system is illustrated in Figure 4.7. SRG based wind energy conversion system consists of SRG machine model, single switch per phase converter, MPPT block, pulse generator and stand alone DC load. SRG machine model is developed by utilizing the data like inductance, flux linkage and torque obtained from analytical model. The mechanical torque required for SRG is supplied from MPPT block which computes torque corresponding to maximum power for the given speed. The stator coils of SRG are excited through single switch per phase converter topology and SRG delivers power to the stand-alone DC load. SRG output varies with wind speed and it is regulated using buck regulator before supplying it to the stand alone load. The response of SRG based wind energy conversion system for the wind profile with three different wind velocities varying in steps at the interval of 0.5 seconds is shown in Figure 4.8.

15 Figure 4.7 MATLAB/Simulink model of SRG based wind energy conversion system 99

16 Figure 4.8 Response of SRG based wind energy conversion system 100

17 101 From the response it is clear that for the change in wind velocity from 10 m/s to 12 m/s, at time=0.5 second, the speed of SRG is increased. As a result, generated voltage, current and power are increased. For the decrease in wind velocity from 12 m/s to 8 m/s at time=1 second, SRG speed is decreased. 4.5 SUMMARY A small power switched reluctance wind energy conversion system supplying stand alone DC load is modelled in the MATLAB/Simulink environment. The wind turbine with HCS MPPT control technique to extract maximum power from the wind turbine is modelled. Coding is developed in MATLAB to obtain the response of wind turbine with MPPT capability. Equation of the optimal line to obtain optimum power for different rotor speed is obtained using curve fitting technique. The response of SRG based wind energy conversion system with three different wind speeds is analyzed and the results are reported. From the results, it is apparent that the low power switched reluctance wind generation system gives satisfactory performance and therefore, SRG is appropriate wind generator for a small wind electric conversion system.