Fuzzy Pitch Angle Control of Wind Hybrid Turbine

Transcription

1 2813 XXIV International Conference on Information, Communication and Automation Technologies (ICAT) October 30 - November 01, 2013, Sarajevo, Bosnia and Herzegovina Fuzzy Pitch Angle Control of Wind Hybrid Turbine To Power Quality Improvement Saeid Ghaderi *, Nastaran Vasegh **, Reza Ghandehari Faculty of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran * Saeid.ghaderi@srttu.edu, ** n.vasegh@srttu.edu Abstract Wind energy is not constant and because of nonlinear effect of wind speed in output power of windmill, the generated power of wind turbine generators (WTGs) fluctuates. In order to reduce fluctuation, different methods are available. This paper presents a control strategy based on fuzzy modeling in different wind speeds to control frequency and together with a hybrid system that controls voltage, can improve power quality of a wind-hybrid power generation system. The simulation results show the effectiveness of the proposed method. Keywords fuzzy modeling; pitch angle control; wind turbine. I. Introduction Renewable energy sources are receiving considerable attention throughout the world since they are environmentally friendly and easy in access. Employing wind as one of the renewable energy source, that is available in virtually every nation in the world, is increased rapidly. It is safe, clean and abundant. At present the wind power costs together with incentives and financing options to support the renewable energy facilities, make wind energy sources competitive with conventional generation sources and it is believed that wind energy will be the most cost-effective source of electrical power in the future [1]. The output power of windmill is proportional to the cube of wind speed. It produces a fluctuating output power. The fluctuations of output power adversely affect the power quality in the distribution system, especially frequency and voltage [2], [3]. Different works have been done to improve the quality of power in a wind generation system, for example in [4] an intelligent adaptive control is used to maintain the quality of power in an autonomous wind-diesel power system. In [5] a fuzzy LQR controller is used to control the quality of a wind hybrid power system, but they have drawbacks: power system in [4] consists of the wind turbine having the induction generator, the diesel engine, synchronous generator, superconducting magnetic energy storage, the dumpload and power system in [5] consists of the wind turbine, the induction generator, the diesel engine, synchronous generator, the battery storage system, the dumpload and the consumer load which means there are many compensators used for power quality improvement in these papers. In another work [6], authors used a PID controller and a bacteria foraging optimization technique for controller parameter optimization in a wind diesel hybrid system, its drawback is that it is applied only for a short range of wind speed. This paper solves these problems. There are different purposes in pitch angle control. Reference [7] uses pitch angle control for maximum energy extraction and in [8] it is used for output power leveling. Pitch angle is controlled to improve power quality in [9]. In this paper a system based on input-output data is used for pitch angle control that keeps frequency in a desirable range and together with a hybrid system that keeps voltage in its nominal value result in power quality improvement. Since voltage regulation is achieved by a hybrid system similar to [4]-[6], so it should be mentioned that our contribution in power quality improvement is our method in control of frequency. The rest of this paper is organized as follows: section II provides a configuration of the hybrid system and equations. Section III describes the power quality control and the fuzzy system. In section IV the effectiveness of the proposed method is demonstrated. Conclusions are brought in section V. II. System Description Our plant consists of the wind turbine having the squirrel cage induction generator (IG) as the energy conversion system and a synchronous generator (SG) as shown in Fig. 1. The scheme is simulated using system models in SimPower of MATLAB software [10]. Fig. 1. The wind-hybrid generation system The windmill output power is given by [11] /13/$ IEEE

2 where is wind speed, is air density, is cross section of rotor for a windmill and is the power coefficient. In [10], the power coefficient is approximated by To achieve the second control target (voltage regulation), hybrid system is used. Here SG as a condenser regulates voltage. Fuzzy system, based on input-output data is used to control frequency for a fixed power via pitch angle control. From (4), and from (5) and also because is proportional to, we have with It should be noted that when there is a gearbox between windmill and IG, the relationship of rotor speed between windmill and generator is expressed as the following equation [13] by using the gear ratio, where are constant coefficients given in table I, is pitch angle and is tip speed ratio given by where R is radius of windmill and is angular speed of windmill rotor. The frequency is related to generator rotor speed as follows [12]: and when there is not a gearbox, they are equal. Since in this work we deal with parameters in per unit, and are the same. Now in order to regulate frequency, the fuzzy system must be designed such that becomes fixed. From (1), (2) and (3) we have a nonlinear equation Since the load power is considered constant so for a constant load, let. Because the left hand side of (7) is constant, can be written as a function of and or p is the number of poles and induction generator. is angular speed of rotor of TABLE I. POWER CONSTANT COEFFICIENTSABBREVIATIONS The synchronous generator is connected in parallel to the induction generator as a condenser with the parameters in table II. TABLE II. SYNCHRONOUS GENERATOR PARAMETERS Nominal Power Nominal Voltage Nominal frequency 300 KVA 480 V 60 Hz III. Fuzzy Controller Design In this section a fuzzy controller is proposed to control the pitch angle for regulating output frequency in the desired range for all wind speeds and the way how the output voltage is regulated is explained. The function can be considered as inverse of (7) for a fixed windmill output power. Since it has nonlinear terms, we approximate it by a fuzzy function [12]. To do this, and must be varied in admissible ranges, then as a nonlinear function of them in a search space should be calculated. The admissible ranges for and is separately explained in the rest and there is this assumption that can have maximum fluctuation 0.1%. 1. Admissible range of is a parameter that depends on the weather conditions of nation of wind farm. In this paper it is assumed that wind speed can vary between 9.5 m/s to 20 m/s and can be measured. 2. Admissible range of From (6), for regulating frequency, must be fixed. So for different wind speeds we choose a proper that

3 keeps within a specified range. The specified range for frequency is. From (4) must be determined to choose. And since and are the same, we determine from (5). By considering the (torque, rotor speed) curve of an IG shown in Fig. 2, the specific range for is chosen. The synchronous generator which is controlled by its excitation keeps the output voltage at its nominal value. Summary of the frequency control method: One of the control objectives of this paper is keeping frequency in a specified range which is the contribution of the paper. The specified range for frequency is chosen by considering the (torque, rotor speed) curve of an IG. On the other hand, determination of speed in this curve results in determination of. By a known, can be calculated for different wind speeds since the wind power is considered fixed. In this paper is calculated for more than points. By considering these points plus the other points obtained for transient conditions, fuzzy system is designed. The membership functions are shown in fig. 4 and 5. Fig. 2. The (torque,rotor speed) curve for an IG. Now by determining the admissible ranges of parameters, can be calculated. Obtaining data for transient conditions: When the system starts we have, which means that, so in order to have a full data set that can be applicable for transient conditions, (i.e. when is less or greater than the specified region) the following data are also added to data achieved before. Fig. 4. Rotor speed membership function When, is set to zero in order to make wind turbine extract more energy and rotate with higher speeds. And when, is set to ( is maximum pitch angle in a particular wind speed) in order to make wind turbine extract less energy that results in less speeds for rotor rotation. By these sets of data, the fuzzy function can be obtained by using Fuzzy toolbox of MATLAB software. To use this toolbox, 3 fuzzy Gaussian membership functions are utilized for each parameter. The overall control structure is shown in Fig. 3. Fig. 5. Wind speed membership function IV. Simulation Results The wind turbine generator parameters used for the simulation are given in table 3. Also we use. After producing data generated in section III, MATLAB built-in functions (genfis1 and anfis) are used to build the fuzzy system [10]. TABLE III. TURBINE PARAMETERS Nominal Power Nominal Voltage Nominal frequency 275 KW 480 V 60 Hz Fig. 3. The control structure. Our method is compared with a control strategy in which it uses a secondary-load power bank and a proportionaldifferential controller to produce an output signal representing the required secondary load power for the frequency control.

4 Black curves are of the proposed method. The wind speed used for simulation is given in Fig. 6. The output of fuzzy and fixed output power is generated according to wind Fuzzy system produces pitch angle as the output and keeps the Fig. 6. Wind speed. Fig. 7. The controlled pitch angle. controller (controlled pitch angle ) is shown in Fig. 7. In Fig. 8, the curve named Wind power shows that power captured from wind is remained fixed in the proposed method however the power from the other method is changed, and the curve named Condensor reactive power shows that a fixed amount of reactive power is needed to regulate voltage in the proposed method while a variable amount of reactive power is needed for the compared one, also the curve named Main power shows a smoother supply of power is fed to the fixed load in our method. Fig. 9 shows that frequency is within the specified range although wind speed varies while a fluctuating frequency is the result of the other method. Three phase voltages are at their nominal values by means of the condenser which one of them is shown in Fig. 10. V. Conclusion A new method in pitch angle control is proposed based on designing inverse fuzzy system. Data for a specific frequency Fig. 8. The comparison of output power by proposed method (black) and the method in [10] (blue). system in a particular operating point while wind speed varies and by means of a condenser that makes the hybrid system,

5 voltage regulation is achieved. The simulation results showed that power quality is obtained. Fig. 9. Comparison of output frequency by proposed method (black) and method in [10] (blue) [3] R. Hunter and G. Elliot, Wind-Diesel Systems: Cambridge U.K.: Cambridge Univ., [4] H.S. Ko, K. Y.Lee, ~11.J. Kang and H.C Kiln, "Power quality control of an autonomous wind-diesel power system based on hybrid intelligent controller," Neural Networks, vol. 21,2008, 2008, pp [5] H.S. Ko and J. Jatskevich, "Power quality control of wind-hybrid power generation system using fuzzy-lqr controller," IEEE Trans. Energy Conversion, vol. 22,2007, 2007, pp [6] K. S. Sandhu and S. Sharma, "Power quality improvements in wind diesel hybrid systems using bacteria foraging optimization technique for controller parameter optimization," Int. J. Energy Engineering, vol. 2, 2012, pp [7] E. Muljadi and C. P. Butterfield, "Pitch-Controlled variable- speed wind turbine generation," IEEE Trans. Industry Application,, vol. 37, 2001, pp [8] T. Senjyu, R. Sakamoto, N. Urasaki, T. Funabashi, H. Fujita and H. Sekine, "Output power leveling of wind turbine generator for all operating regions by pitch angle control," IEEE Trans. Energy Conversion, vol. 21,2006, 2006, pp [9] A. S. Yilmaz and Z. Ozer, "Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks," Expert systems with applications, vol. 36, 2009, pp [10] Matlab software demo [11] W.-M. Lin, C.-M. Hong, F.-S. Cheng, "Fuzzy neural network output maximization control for sensorless wind energy conversion system," Energy, vol. 35,2010, pp [12] S. J. Chapman, Electric Machinery Fundamentals, Mc-Graw Hill, Fourth Edition, [13] T. Senjyu, Y. Ochi, Y. Kikunaga, M. Tokodume, A. Yona, E. Billy Muhando, N. Urasaki, T. Funabashi, " Sensor-less maximum power point tracking control for wind generation system wiyh squirrel cage induction generator," Renewable Energy, vol. 34, 2009, [14] L.X. Wang, A course in fuzzy systems and control, Prentice-Hall International, [15] The MathWorks, Inc. Published with MATLAB 7.9 Fig. 10. Output voltage of one phase [pu]. References [1] J. Earnest, T. Wizelius, Wind Power Plants and Project Development, PHI Learning Private Limited, New Delhi, [2] L. L. Feris, Wind Energy Conversion System, Englewood Cliffs, NJ: Prentice Hall, 1990.