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Canadian Journal on Electrical and Electronics Engineering Vol. 2, No. 6, June 2011

Design Optimal of Adaptive Control and Fuzzy Logic Control on Torque-Shaft Small Scale Wind Turbine

Ali Musyafa(1), I.Made Yulistiya Negara (2), Imam Robandi(3)

Abstrac In this paper carried adaptive controller design and fuzzy logic control (FLC) is applied to the wind turbine shaft torque. Design goal to improve the performance of wind turbines to increase efficiency. Control system designed to facilitate adaptive estimation of the "shaft torque" wind turbines. Estimated torque reference applaid to provide torque to the subsequent induction machine connected to the turbine through a gearbox arm. Adaptive controllers, linear feedback, designed to ensure the existence of a linear relationship to the turbine system with a guarded the turbine speed and load changes from energy users. Fuzzy Logic Control aalso developed which is intelligent control in wind turbine systems. Changes input are designed based on wind data from the next field to find out for imbalance, dynamic stability and tracking of changes in turbine speed. Reference speed controller is a function of wind speed varied selected to ensure that the system can produce optimal energy. The results show that the performance of control systems designed to give a good response to fluctuating wind speeds. FLC able to produce a better system performance in line with expectations.

I.

INTRODUCTION

Keywords - Wind turbine, Adaptive control, Linear feedback, PID Control , Fuzzy Logic Control (FLC).

______________________________________________________________________

Wind speed and other variables that will determine influence appropriate alternative for the implementation of control systems in a plant[1-2].The geographical position of a region also affects the wind speed, for example in the upstream or coastal areas. Local topology can be used to conduct a study of wind turbine applications[2-3]. There are many types of wind turbine configurations that can be used to extract the energy and connection with the use of synchronous or asynchronous machines, stall regulation and pitch regulation system [45]. Variations in wind speed can produce exctract for further wind power into electrical energy transformation and electricity sent through the network with a particular specification. [6-7]. Wind speed data used in this study is the sampled data from the Meteorological Station: Surabaya Juanda Meteorology Station, Location: '070 23 '05 "70 S: 1120 47' 02" 68 E, elevation: 28 meters, Element: Wind, measuring tool: Anemometer, Units: Knots, data from BMG converted into units meters per second (m/s) [8]. Wind speed data are grouped into 3 zones based on the level of speed. 1zone speed range (0,12) m/ s. 2 zone speed range (2,1-3,9) m/s, and 3 zone speed range (4-7) m/s. One of the wind speed profile is shown in Fig.1.[1-2].

6.5 6 5.5 5 V (m/s) 4.5 4 3.5 3 2.5 2 0 50 100 time (s) 150 200 250

Ali Musyafa' is with Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Kampus ITS Keputih Sukolilo, Surabaya, Indonesia, 60111 and Engineering Physics Department, Institut Teknologi Sepuluh Nopember, Kampus ITS Keputih Sukolilo, Surabaya, Indonesia, 60111 (corresponding) phone: +62-31-5947188, fax: +62-31-5923626; e-mail: [email protected]). I. Made Yulistya Negara2 and Imam Robandi3 are with Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Kampus ITS Keputih Sukolilo, Surabaya, Indonesia, 60111.

1

Fig. 1. INPUT WIND SPEED (m/s)

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Wind turbine components comprising a rotor consisting of a number of fins (blade) and mounted on the front of a swivel shaft (hub/shaft) connected at the rear of the turbine through a gear box. Axis swivel out of the gearbox is coupled to a generator which then generators change mechanical energy into electrical energy [9-10]. A rotor blade's wind turbine is composed of 3 fins which serve to capture wind energy in the form of mechanical energy. The problem in this section is the aerodynamic design of effective and efficient, and tries hard materials durability and fins and a propeller. Gearbox: gear system includes a low turn change the speed of the propeller (about 100 rpm) to a high turn rate (> 500 rpm) for input generator.[11-14].

Fig. 2. BASIC CONCEPTS ADAPTION

II. METODOLOGI

Control algorithm in the adaptive system consists of two parts; basic algorithms and adaptation algorithms. In adaptive control system there are two levels of the system. The lower level are the basic controls that are (executor of the control algorithm) that directly receive data from the plant and determine the control decision, and a higher level called adaptation (executor has the adaptation algorithm). The second level of control both the basic control level and control level of adaptation, they will improve the system performance is often referred to as a two-tier system. The system shown in Figure 2. Adaptive general controls consist of a control algorithm based on the adaptation algorithm. In this study the basic control algorithm is Feedback Linearization. The use of favorable feedback, because linearization control linear relationship obtained from additional input v (which is previous controller output signal) of the variable speed wind turbine angle (). Fig. 3 show a model of wind turbine system and Table 1. Show wind turbine parametres. Feedback linearization is a control system that can manipulate variable (MV) of torque is the model for process variable (PV) is a constant (). In the wind turbine control system based on braking system has a central role in the manipulation of the turbine torque. Original torque (Tt) is derived from turbine gearboxes, while ( Tem ) torsion sub-system. The equation as show as eq. 1. Things that need to be observed is the condition Tt changing conditions caused by changing wind speeds at all times. Thus, to obtain the optimum control results, required sensing system that monitors the change of Tt, and will affect the magnitude Tem. Thus the adaptation algorithm is needed to monitor changes in Tt is caused by input changes. Furthermore adaptation algorithm as show in eq. 2-3. Understanding adaptive mentioned is a change / update the quantities here`in`after Tt change (adaptive) to wind speed input.

Fig. 3. A PROTOTYPE OF WIND TURBINE Tt is very usefull adaptation for the manipulation Tem , to obtain an optimal control system. In the overall system there are two Tt; Tt = results from turbine gearboxes and bars Tt = , from the adaptation law. & J R = Tt - Tem (1)

& e = -ke -

1 ~ Tt JR

(2)

& e ^ Tt = JR

(3)

The wind feed on the blade will form the aerodynamic lift and thrust produced. This situation resulted from the rotor wind turbine torque and wind power show in eg.4-5 ;

Trot =

Paero

rot

1 = R 3C p ( , pitch ) 2

(4) (5)

1 3 Paero = R 2 u eq C p ( , pitch ) 2

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The relationship of torque and angular velocity of the turbine is shown as ; & J R = Tt - Tem (6)

III. CONTROL SYSTEM DESIGN

Control system block diagram-torque shaft designed in wind turbine using adaptive feedback linearization control shown in Fig. 4. Wind velocity (), is the system input variables. Changes input system will be controlled by a PID controller and feedback system will next controlled by the adaptive law, so the system will continually adapt, or to vary the amount of torque from a wind turbine gearboxes. With the entry has changed and still be able to produce output torque in accordance with the Field Oriented Control (FOC). Fig.5. Therefore a system actuators turbine braking system, the process variable in the equation of wind turbine model will generate output control system of the shaft angular velocity. Fuzzy logic is a methodology of problem-solving control system that can be applied to human language (high, low, long, short, etc.) Which allows eliminating the difficulty of mathematical language Fuzzification is a process of mapping from the input of the set of strict (crisp) into the form of fuzzy sets for a particular conversation universe? The data was then converted into mapping linguistic forms according to the labels of fuzzy sets that have been defined for the system input variables Fuzzy Inference. Engine (Fuzzy processing) is the core of a fuzzy logic controller that has the ability as humans to make decisions Defining the size of membership and degree of linguistic variables of the action undertaken to control each of the control rules based on implication functions are used. Defuzzification has transformed the functions are fuzzy conclusions into crisp signals (which are real) by using the defuzzification operator. Mapping ripples fuzzy control action (fuzzy domain name) into the control action of the non-fuzzy (crisp) defuzzification base( Table-2). Fuzzy logic control strategy proposed for the control of wind turbine shaft torque. By combining PI-fuzzy controller, the control point can be introduced, which are designed carefully to both the on-line and off-line which can then be applied to the rotation shaft torque actuation. FLC strategy can overcome the possible parameters of uncertainty, lack precision and what was not sure in some mathematical models based on the human knowledge.

& e = - ke -

1 ~ Tt JR

(7)

e & (8) T^t = JR JR is a combination of generator and turbine inertia is delivered by the shaft, a block diagram of field oriented control as follows, Block diagram of linear feedback systems, with a selected constant = 2 x106. Relationship adaptation system shown in the eq.7-8;

Table 1 WIND TURBINE PARAMETERS [2]

Item Value

Wind turbine model Rated power (kW),v=5 m/s Rotor diameter (m) Hub heigh (m) Swept area of rotor (m2) Cut-in wind speed ( m/s) Rated wind speed ( m/s) Cut-out-wind speed (m/s) Rotor speed ( rpm ) Tower type Generator type Rating power (KW) Rating volt (V) Rating amp (A) RPM (N*M) Power Weight (Kgs)

ALFA-IR 001 50 W 1 10 0,785 3 4 10,0 m/s 20-500 Turbular ALD-50.PMA. 0,05 14/28 3,57/1,79 500 < 0,15 >65 % 6

Integral Time Absolute Error (ITAE) is one of the methods used to measure the absolute error of the performance of a system. Shown in eq.4 ; This criterion is the performance index of the most easily applied. If this criterion is applied, the damping system for damping down and cannot be optimum. Optimum systems based on these criteria are a system that has damping characteristics and meet the following transient response ; Like the previous criteria, a large initial error in the unitstep response has a small weight, and the error happens next will affect the transient response. ( 9)

Vwind

Fig. 4. BLOCK DIAGRAM OF ADAPTIVE SYSTEM

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FLC used for shaft speed control to tuning torque from wind turbines. More FLC to variations robust turbine parameters and have a better ability to resist interference in the internal and external, and provide fast dynamic response of stability. FLC allows can be used for speed control PI tuning on-line and can accept the values that have been scale in principal wich corelation in error veleocity and delta error velocity. Output here updated by a gain PI controller (Kp and Ki) which is based on a set of rules to maintain the exellent control perform remain even when the parameters and the lack variation linear actuation Each input from FLC consists of five triangular membership functions with the same width and overlapping. The first output (Kp) has three triangular membership functions, while the second output (Ki) has five memberships function. Inference rules based on rule 25. Parameters of the FLC calculated by trial and error to ensure optimum performance.

Table 2. RULES BASE A TWO-DIMENSIONAL LINIER

Fig. 6. LINIER FEEDBACK SYSTEM

Fig. 7. CONFIGURATION CONTROL ADAPTIVE

Fig. 8. BLOCK DIAGRAM PID-ADAPT. CONTROL

Fig. 5. BLOCK DIAGRAM OF FOC.

Fig. 9. BLOCK DIAGRAM OF FLC.

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Canadian Journal on Electrical and Electronics Engineering Vol. 2, No. 6, June 2011

Block diagram of ccontrol system in wind turbine shaft torque for PID Adaptive control is shown in Figure 8. and block diagram for the fuzzy logic control showed in Figure 9. The two systems were running to determine the performance of each system block and overall system performance. As a tool to test the control strategies and evaluate the performance of control systems using fuzzy logic rule base as shown in Table- 2.

Fig. 12. ADAPTIVE CONTROL RESPONSE FOR WIND SPEED INPUT (4.0 -7.0) m/s

IV ANALYSIS

At this stage testing control systems with Simulink, the first test conducted on the open loop system. At this stage the obtained parameters Kp, Ti, and Td, which in turn is used for PID control tuning. Further testing PID control system using 3 types of range: (0-2m/s),(2.13.9m/s),and(4-7 m/s).Each range tested by giving interference with the value 0%, 10%, 20%, and 30% of set point. PID tuning methods used are continuous method Cycling Method (oscillation method). My next calculated value and Tu. Further included in the formulation of the PID in Table 3, and the PID parameter values obtained; Kp, Ti, and Td. By the same way, obtain PID parameters in range 1-3.

Table 3. PID TUNING WITH CONTINUOUS CYCLING METHOD Kp Ti Td Range 1 417.6400 0.2100 0.0525 Range 2 423.5290 0.2050 0.0513 Range 3 441.1765 0.2000 0.0500

Fig. 13. FLC CONTROL RESPONSE FOR WIND SPEED INPUT (0-2) m/s

Fig. 14. FLC CONTROL RESPONSE FOR WIND SPEED INPUT (2.1 -3.9) m/s

The respons of system would like to identify the system performance satisfaction in the PID-adaptive and FLC controllers with test in all range wind speed as follows Fig. 10-15.

Fig. 12. ADAPTIVE CONTROL RESPONSE FOR WIND SPEED INPUT (4.0 -7.0) m/s From the eksperiment, the performance index wind turbine system with PID-adaptive and FLC can demontrated. Performance test that is performed when the system is running without any interruption and interference with the system. The criteria used as a benchmark assessment of the maximum overshoot (%), settling time (s), and steady state error (%) and ITAE. From the calculations can be show on the table 4-5. Table 4. PERFORMANCE 0F PID-ADAPTIVE AND FLC CONTROL

Fig. 10. ADAPTIVE CONTROL RESPONSE FOR WIND SPEED INPUT (0-2) m/s

Fig. 11. ADAPTIVE CONTROL RESPONSE FOR WIND SPEED INPUT (2.1 -3.9) m/s

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Table 5. COMPARISON VALUE ITAE OF ADAPTIVE AND FLC CONTROL

V. CONCLUTION

The FLC action showed a better response than PID adaptive control, this is indicated by the time the achievement of steady state (ts) that is shorter, the maximum overshoot (Mp), and steady state error (ess.) A smaller. ITAE value of the FLC smaller than the PIDadaptive controller system, this shows that the resistance (robustness) systems using FLC better than PID-adaptive controllers, both associated with a disturbance and no disturbance.

REFFERENCES

[1] Ali Musyafa, Imam Robandi,2009, " Local ShotTerm Wind Speed Prediction in the Nganjuk City (East java) using Neural Network" Proceeding of 6 th International conference Numerical Analysis in Engineering (NAE, Mei 2009), Lombok-Indonesia. [2] Ali Musyafa, A.Harika, I.M.Y.Negara, Imam Robndi,2010, "Pitch Angle Control of Variable Low Rated Speed Wind Turbine Using Fuzzy Logic Control" International Journal Of Engineering & Technology IJET-IJENS Vol:10 No:05, pp.21-24. [3] F. D. Bianchi, R. J. Mantz, C. F. Christiansen, 2004, "Power regulation in pitch-controlled variable-speed WECS above rated wind speed", Renewable Energy IEEE Vol.29 pp.1911-1922. [4] Onder Ozgener, 2006, " A small wind turbine system (SWTS) application and its performance analysis analysis" Energy Conversion & Management, Elsevier Vol.37, pp. 1326-1337.

[5]Y. Himri et.at. ,2008,"Wind power potential assesment for three locations in Algeria" Elsevier-Reneable & Sustainable Enegy Reviews , Vol.12 2495-2504. [6] MD Arifujiyaman, et all. ,2005, " Modeling and Control of Small Wind Turbine" CCECE/CCGEL Saskatoon, IEEE.N0.5, May pp.778-781. [7] R.Ata, Y.Kocyigit,2010," An adaptive neuro-fuzzy inference system approach for prediction on tip speed ratio in wind turbines", Expert systems with Application , Elsevier Ltd. Vol. 37 pp. 5454-5460. [8] H. Suharta,2009, " Enegy Data-indonesia", B2TEBPPT, Puspitek Serpong, Tangerang(15314), Indonesia, Wind Energy Workshop JHCC.18-19 June ,Jakarta. [9]Yousif El-Tous,2008, " Pitch Angle Control of Variable Speed Wind Turbine", American J.of Engineering and Applied Scciences Vol 2. pp.118120. [10] K.R. Ajao, 2009, " Comparation of Theoritical and Experimental Power outpus of a Small 3-bladed Horizontal-axiz Wind Turbine" , Journal of American Science Marsland Press 5(4): pp. 79-90. [11]V Calderaroa, V Galdia,A.Piccoloa, and P. Sianoa, , 2007, "A fuzzy controller for maximum energy extraction from variable speed wind power generation system", DIIIE, University of SalernoItaly, 31 October, Vol.1.pp,84-89 [12] Alex Murey,2011, " Wind turbine instalation have to clear" Britis W.E. association, http://www. obengware.com. (access date) [13] J. H. Laks, L. Y. Pao, and A. D., 209, " Wright, Control of Wind Turbine:Past,Present, and Future", US National Science Foundation(NSF Grant CMMI0700877) [14] A. S. Yilmas, Z. Ozer, 2009, "Pitch angle control in wind turbine above the rated wind speed by multilayer perceptron and radial basis function neural network, Expert System with Application Vol.36, pp.9767-9775. Annexes ;

Fig.13 . Wind turbine experimen

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BIOGRAPHIES

Ali Musyafa', Was born in 1960 in Jombang, Indonesia. He recived B.Sc. degree in Engineering Physics from Sepuluh Nopember Institute of Technolgy , Surabaya, in 1986, and M,Sc, degree in Electrical Enginering from Bandung Institute of Technology, Indonesia in 1990. He is currently P hD. Student in Department of electrical engineering, Sepuluh Nopember Institute of Technology. His current reasearch interest includes renewable energy generation and control. I Made Yulistya Negara, Was born in 1970 in Negara, Indonesia. He recived B.Sc. degree in power engineering from Sepuluh Nopember Institute of Technolgy , Surabaya, Indonesia in 1994, and M,Sc, degree in Electrical Enginering from Untersuchungen zu Teilentladungsmessungen bei Gleichspannung, in Universitaet Karlsruhe, Deutsch, in 2001. and PhD degree in Department of Electrical Engineering from the Kyushu University, Japan,2006. Imam Robandi, Was born in 1963 in Gombong, Indonesia. He recived B.Sc. degree in power engineering from Sepuluh Nopember Institute of Technolgy, Surabaya, in 1989, and M, Eng., degree in Electrical Enginering from Bandung Institute of Technology, Indonesia in 1994. and Dr.Eng. degree in Department of Electrical Engineering from the Tottori university, Japan, 2002. He is currently Profesor in Department of Electrical Engineering, Sepuluh Nopember Institute of Technology. His current reasearch interest includes Stability analysis of multimachine power system using LQR, fuzzy logic, and artificial intelegent control.

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