Wind energy extraction and conversion: optimization through variable speed generators and non linear fuzzy control

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1 Wind energy extraction and conversion: optimization through variable speed generators and non linear fuzzy control Paulo Costa Instituto Politécnico de Viana do Castelo, Avenida do Atlântico, 4900 Viana do Castelo, Portugal, tel : , pjc@fe.up.pt António Martins, Adriano Carvalho Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias Porto, Portugal, tel , ajm@fe.up.pt, asc@fe.up.pt Summary The maximization of wind energy extraction can be obtained using variable speed electrical generators in conjunction with non linear control algorithms. This paper presents an incremental speed control method which is independent from both wind speed measurement and specific systems characteristics. The algorithm is based on the dynamic behaviour of the electrical generator and uses fuzzy logic as a non linear reasoning tool. The simulation results obtained with this controller applied to a double feed induction generator show that it is possible to increase the efficiency in the conversion from wind power to variable speed and torque. Key words : power coefficient, variable speed,, fuzzy control, active power, wind farm Introduction Wind energy is an important source of electrical energy in years to come. Its main advantages come from the fact of being a renewable and environmental-friendly energy. At the beginning it was cheap and very robust but the generated power quality was poor. Most of wind power installations were limited to a few hundreds kilowatts connected to distribution grids [1]. Wind turbines and farms grew in size and ratio from the few hundreds kilowatts to megawatst size. The increased rated power of wind farms to areas with good wind resources leads to a new problem approach to which extent the wind power interferes to the power system. The performance of a wind generation system can be separated into three main conversion stages. The first one is the transformation of the wind speed kinetic energy in the turbine speed and torque. In the second one, the turbine mechanical energy is transformed by an electrical generator, which can be an asynchronous or synchronous machine. The performance of these two power conversions will strongly impose the global performance of the wind power system. Generally, the grid connection is made by an electronic power converter with high efficiency. So, it is very important to maximize the performance of the first conversion, what can be done by using variable speed generators. Many previous researches have focused at the same goal, maximizing wind power extraction, by three different methods: tip speed ratio control, power signal feedback control and hill-climb searching control [2], [3], [4] and [5]. The paper presents an incremental speed control method which is independent from wind speed measurement as well as from specific systems characteristics, and it can be applied to all kinds of turbines, with small or high inertia. The performance of the first energy conversion is measured by the power coefficient parameter; high performance is obtained working close to maximum value for it. The presented algorithm is based on a simple but robust approach: if the power on the turbine input shaft is equal to the generator electrical output the resulting speed slope is zero, i.e. the torque reference is maintained. If the turbine shaft accelerates, it is possible to increase the generator torque and extract more energy from the mechanical system. Otherwise, if the shaft decelerates the generator torque should be reduced. Fuzzy logic is the right tool to use for implementing this control strategy, due to the complexity and non linear system behaviour. The fuzzy controller has three inputs: speed error, speed slope and last torque change, and one output variable: the change of the generator torque reference. For achieving good results it is necessary to avoid maximum relative points, which the method guarantees.

2 Wind turbines A wind turbine is characterized by its power-speed characteristics. For a horizontal axis wind turbine, the amount of power P t that a turbine is capable of producing is given by P 1 3 t = * C p * ρ * A * v (1) 2 where ρ is the air density, A is the swept area of the turbine and v is the wind velocity. The C p parameter is called the power coefficient and is dependent on the ratio between the linear velocity of the blade tip (R*W t ) and the wind velocity (v). This ratio, known as the tip-speed ratio, is defined as wt * R λ = (2) v where R is the radius of the turbine. This kind of application is designable with constant or variable speed. Different types of wind turbines are available on the market. The different types of wind turbines have their own advantages and disadvantages. Fixed-speed wind turbines normally cause a voltage drop during start-up [6]. The voltage drop is mainly caused by reactive power consumption during magnetization of the generator. Another problem concerned to wind turbines with fixed speed is the flicker produced during normal operation of the wind turbine. Once this method the energy extraction is optimized for one point of operating, it means that for only wind speed is achieved the maximum value of extraction. The variations in the generated power are mainly caused by the wind turbines and the tower shadow effect. These variations can lead to flicker emission. Variable-speed wind turbines can reduce these power variations and eliminate the flicker caused by power pulsation, becoming possible to maximize the energy extraction using an appropriated control method. Fuzzy logic Modern systems include large and complex subsystems and sophisticated technical devices that need to be controlled. They require the development of mathematical models. However, these models are rather complicated and include some vagueness. So it is hard classical mathematics that processes these models [7]. The approach of fuzzy logic is based on a mathematical framework where imprecise conceptual phenomena in modeling and decision making may be precisely and rigorously studied. It lets mathematical models at describing rather unmodelled situations and it finds solutions for not formulated problems. Fuzzy logic is applied to wind farm control with the goal of pass-through the complex, non-linearity and uncertainty of these control systems. In [8] are present some limitations of conventional controllers as: nonlinear models are computationally intensive and have complex stability problems. A plant does not have accurate models due to uncertainty and lack of perfect knowledge, uncertainty in measurements and multivariables and multiloops systems have complex constraints and dependencies. Control method The control algorithm has as main objective to keep the tip speed ratio, always inside of a small bandwidth near to the maximum value, independently of the existence of the pitch controller, because it has a lower time constant. So, the emphasis of the control is on turbine rotational speed and its time behavior due to the balance between aerodynamic torque, energy from the wind, and electric torque produced by electrical machine, energy extracted to the turbine shaft. Figure 1 illustrates the global framing of this controller in the wind system. Figure 1. Integration of power coefficient controller on the glogal wind control structure.

3 The control algorithm, figure 2, is based on a global controller that receives all information, it prepares the inputs variables and it sends them to his nuclear fuzzy controller system. This module processes the behaviour of the inputs variables and finds the right solution to apply in the wind turbine speed to achieve the main goal - maximize the operation point by achieving a maximum value to the power coefficient, increasing power extraction. The process begins with a value for wind speed. Figure 2: Algorithm structure of the power coefficient optimization The approach of the authors is based on the development of a fuzzy controller, what implies an appropriate structure and rules of inference for this controller, at optimizing operation of the wind turbine. The structure used is presented in figure 3. Linguistic variables are used to translate real values into linguistic values. The possible values of a linguistic variable are not numbers but so called 'linguistic terms. The fuzzy controller has three inputs: speed error (error), speed slope (dv) and last torque change (ldt), and one output variable (output): the change of the generator torque reference.

4 Figure 3. Fuzzy logic controller The rules' 'if' part describes the situation, for which the rules are designed. The 'then' part describes the response of the fuzzy system in this situation. The degree of support is used to weigh each rule according to its importance. The wind turbine controller is constituted by the following rules, figure 4: a) b) c) Figure 4 Fuzzy rules considering: a) speed error negative, b) speed error null and c) speed error positive With the following significance: N - Negative NP Negative Small PP Positive Small Z Zero NM Negative Médium PM Positive Médium P Positive NG Negative Big PG Positive Big The resulting control surface using the min-max and centroid operators is shown in figures 5, 6 and 7. These figures show a 3D surface result of the applied rules, with the two input variables on horizontal axes, while the output is plotted on the vertical axis. In order to have a three dimensional representation, the third input value is considered constant. In the first figure (figure 5) it is considered that the last torque change has no variation, in figure 6 the speed slope is constant and the last representation (figure 7) takes speed error as a constant value. Figure 5 Fuzzy controller surface (last torque change as a constant input)

5 Figure 6 Fuzzy controller surface (speed slope as a constant input) Figure 7 Fuzzy controller surface (speed error as a constant input) System behaviour Nowadays significant increases in the wind power generation have to make use of new control methods, like fuzzy logic. The power coefficient measures the efficiency of the first conversation of wind into electricity. This coefficient depends on the relationships between wind speed and rotational turbine speed. In this work it is demonstrated that it is possible to increase the energy extraction using the right controller, especially at low wind speeds. This control method imposes the turbine rotational speed to achieve always the maximum value of the power coefficient, independent of the wind speed measurement and specific systems characteristics. The wind speed variation on a wind turbine is very complex and demand sophisticated techniques to optimize the power extraction. In order to show the algorithm validity two different wind turbines were analyzed. The first simulation considers the characteristics of a NEG-MICON NM52/900 wind turbine, figure 8. In the second simulation, figure 9, the Enercon E44/600 wind turbine characteristics are considered.

6 a) b) Figure 8: a) and b) The operation of the controller applied to a wind turbine MM52/900 for optimize the power coefficient. Traces from top: Cp - Power coefficient, P_ref - Power reference, vento - Wind speed and w_rad/s - wind turbine speed.

7 a) b) Figure 9: a) and b) The operation of the controller applied to a wind turbine E44/600 for optimize the power coefficient. Traces from top: Cp - Power coefficient, P_ref - Power reference, vento - Wind speed and w_rad/s - wind turbine speed. Conclusions The control algorithm developed in this work has assumed one goal: whole system performance could be maximized and optimized considering only the control of wind turbine Speed. So, this control method is not based in analytical or empirical expressions but in real effects allowing having a control method independent from systems characteristics. The use of fuzzy logic allows at solving the problems of non-linear and high complexity of the power coefficient behavior. The simulation results obtained with this controller applied to a double feed induction generator show that it is possible to increase the energetic performance of the first conversion and consequently of the all wind power generator system. The developed controller presented in this paper shows that it is controllable to keep on a

8 narrow bandwidth the power coefficient close to its maximum value. Presented results show that, considering present high power turbines, with 4 or 5 MW, this kind of controller is able of achieving significant increase on generated power. References [1] Rosas P. Dynamic Influences of Wind Power On the Power System. Thesis submitted to Ørsted Institute, Section of Electric Power Engineering Technical University of Denmark for the degree of Doctor of Philosophy, March [2] Ermis, M., Ertan, H. B., Akpinar, E. e Ulgut, F. (1992). Autonomous Wind Energy Conversion System with a Simple Controller for Maximum-Power Transfer. IEE Proceedings-Electric Power Applications, Vol. 139, nº 5, pp [3] Chedid, R., Mrad, F. e Basma, M. (1999). Intelligent Control of a Class of Wind Energy Conversion Systems. IEEE Transactions on Energy Conversion, Vol. 14, nº 4, pp [4] Hilloowala, R. M. e Sharaf, A. M. (1996). A Rule-Based Fuzzy Logic Controller for a PWM Inverter in a Stand Alone Wind Energy Conversion Scheme. IEEE Transactions on Industry Applications, 32, nº 1, pp. 57. [5] Wang, Q. e Chang, L. (2004). An Intelligent Maximum Power Extraction Algorithm for Inverter Based Variable Speed Wind Turbine Systems. IEEE Transactions on Power Electronics, Vol. 19, nº 5, pp [6] Manwell JF,Mcgowan JG, Rogers AL. Wind Energy Explained Theory, Design and Application. Wiley: England, 2002; 8: [7] Ross Timothy J. Fuzzy logic With Engineering Applications. McGraw Hill, 1995; 1:1-15, 2: [8] Reznik Leonid. Fuzzy Controllers. Newnes, Oxford 1997; 1:1-9. Acknowledgments The first author would like to thank PRODEP (Programa de Desenvolvimento Educativo para Portugal) for the support of this work.