Chapter 97
Yaw Control of Wind Turbine Using Fuzzy Logic Controller R. Bharani and K.C. Jayasankar
Abstract Wind power generation need some advancement in tracking the direction of high wind speed to generate maximum power all through the day. This paper explains about the maximum wind speed direction tracking using fuzzy logic controller. As the maximum wind speed and direction is sensed using anemometer and wind vane respectively. The present direction of the wind turbine head is sensed, the present wind direction is considered as reference then the error is calculated. The error signal is given to the fuzzy controller to rectify the error and make the wind turbine head to turn towards the direction of the maximum wind. Thus the generator generates maximum power. Keywords Fuzzy controller
Wind turbine Yaw control PID controller
97.1 Introduction Wind energy is accepted as a carbon-emission-free source of renewable energy. Wind blows all throughout the day. It is available in abundance for converting them into electrical energy. In fact, the wind power production spreading, also aided by the transition from constant to variable speed operation, that represents one of the significant factors in the development of wind turbines, involves the development of efficient control systems in order to improve the effectiveness of wind systems [1]. Wind is converted into electrical energy effectively by tracking the maximum wind point to get maximum power output all through the day. The conversion process uses the basic aerodynamics of lift to produce a net positive torque on a rotating shaft. From the point of view of wind energy, the most striking characteristic of the wind resource is its variability. The amount of wind power crucially depends on the speed of the wind, as the power is proportional to the cube of the wind speed. Hence, small R. Bharani (&) K.C. Jayasankar Department of Electrical and Electronics Engineering, Prathyusha Institute of Technology and Management, Chennai 602025, Tamil Nadu, India e-mail:
[email protected] © Springer India 2015 C. Kamalakannan et al. (eds.), Power Electronics and Renewable Energy Systems, Lecture Notes in Electrical Engineering 326, DOI 10.1007/978-81-322-2119-7_97
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differences in the wind speed result in significant differences in the produced power [2]. The wind is highly variable, both geographically and temporally [3]. At a particular wind velocity the wind turbine speed attains maximum to get maximum power. Thus we are going for variable speed wind turbine to produce the electrical power. In fact, variable speed operation increases the energetic efficiency and reduces the drive train torque and generated power fluctuations [4]. Therefore, to control the turbine blade direction few innovative solutions based on soft computing methodology is needed. Thus, we are going for fuzzy system to control the direction of wind turbine towards the direction of the wind. Fuzzy logic theory have found a great variety of applications in control engineering, power systems, telecommunication, consumer electronics, information processing, pattern recognition, signal processing, machine intelligence and so on. The fuzzy control algorithm basically consisted of a set of heuristic control rules and fuzzy sets [4]. The IF-THEN rules play a main role in developing fuzzy rules. The fuzzy control “IF–THEN” rules are often obtained based on an operator’s control action or knowledge [4]. To provide a better tracking effect for system, an approach of fuzzy rule is adopted, which PID parameter is adjusted by fuzzy rule when Wind Turbine Generator is operating [5]. Adjusting of PID parameter will improve the PID controller performance only. To improve the resolution the fuzzy controller is used to control the servo motor to turn the turbine head. The fuzzy rules are developed using MATLAB toolbox and the fuzzy controller is designed and developed. As the number of rules increases the resolution of rotating angle of the wind turbine head too increases. Thus, the turbine head faces the maximum wind direction all the time. The basic block diagram of yaw controller is shown in Fig. 97.1.
Fig. 97.1 Basic block diagram
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97.2 Materials and Methods 97.2.1 Selection of Wind Turbine The power output from the wind turbine is given by following equation 1 P ¼ Cp qAU 3 2
ð97:1Þ
where, ρ Density of air (1.25 kg/m3) Cp Power coefficient A Rotor swept area U Wind speed As by varying the power coefficient and rotor swept area modest increase in power output can be achieved. Instead, to increase the output power drastically the wind turbine should be located on the site with higher wind speed. The basic driving force of air movement is a difference in air pressure between two regions. This air pressure is described by several physical laws. One of these is Boyle’s law, which states that the product of pressure and volume of a gas at a constant temperature must be a constant, or p1V1 ¼ p2V2
ð97:2Þ
The wind speed at heights of 20–120 m above ground is very desirable in selecting the type of wind turbine to be installed according to the power to be generated. The capacity of the wind turbines selected is large wind turbines ranging from 1.5 MW to 5 MW. The synchronous generator is used to generate the power output, which is used to measure the maximum output power produced using yaw control system.
97.2.2 Wind Energy Measurement Wind energy measurements are associated with wind speed, wind turbine rotational speed and electrical signal including voltage, current or collective electrical power. Ultrasonic anemometer is used to measure the wind direction and cup type anemometer is used to measure the wind speed. An ultrasonic anemometer in the nacelle supplies the reference angle to the controller, which sends a signal to the yaw actuators to move the nacelle towards the wind direction [6]. The cup type air speed measurement is used for free air. The present position of the nacelle is detected by the induction potentiometers. To determine electrical energy output it
1000 Table 97.1 Electrical energy measurement
R. Bharani and K.C. Jayasankar Quality
Symbol
Unit of measurement
Current Voltage Resistance Power/Energy
I V R E
Amp Volt Ohm Watt
can be measured either directly as energy in kWh or indirectly by measuring voltage current output of the wind turbine. The lists of electrical energy measuring quantity are listed in Table 97.1.
97.2.3 Yaw System Yaw system consists of two main systems as yaw control system and yaw drive system. Horizontal wind turbines employs yaw mechanism to keep the turbine headed into the wind. Yaw angle is the system of change of tracking the wind direction. It usually turns at a constant speed. Thus, a very simple configuration consisting of several motors working together is enough to position the nacelle. The present position of the nacelle is detected is compared with the direction of the wind at present, an error signal is generated to compensate the difference in angle. This is a closed loop control system to compensate the error. The yaw motor is rotated in clockwise direction to rotate the missile in anticlockwise direction vice versa. The yaw ring gear and yaw induction gears rotation are shown in Fig. 97.2.
Fig. 97.2 Yaw gears
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97.3 Theory and Calculation 97.3.1 Fuzzy Controller and Algorithm The fuzzy control is used to solve complex, nonlinear and multivariable models. They do not need any mathematical model of the plant. It is basically an adaptive and nonlinear control, which gives performance for a linear or nonlinear plant with parameter variation. The fuzzy control algorithm basically consist of a set of heuristic control rules, fuzzy sets and fuzzy logic were used to represent linguistic terms and to evaluate the control rules called conventional fuzzy control or Mamdani-type fuzzy control [4]. The tracking of wind direction is controlled by using Mamdani-type fuzzy controller. The primary benefit of fuzzy systems theory is to approximate system behavior where analytic functions or numerical relations do not exist. Hence, fuzzy systems have high potential to understand the very systems that are devoid of analytic formulations: complex systems [7]. The fuzzy controller is designed and simulated using MATLAB R2013a toolbox. The fuzzy system consists of input set and output set interfaced by the fuzzy inference process. The fuzzy inference process consists of three major steps as • Fuzzification • Rule base evaluation • Defuzzification
97.3.1.1 Fuzzification Fuzzification is the process of finding the membership degrees to which input data belong to the fuzzy sets in the antecedent part of a fuzzy rule [8]. A membership function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1. Wind direction and turbine head direction are the two analog input variables ranging from 0° to 359° and 0° to 359° respectively. There corresponding fuzzy set are in the range of 0 to 5 V and 0 to 30 V respectively. The output variable is torque of yaw actuators to rotate the turbine head. The inputs and outputs are determined by the degree to which they belong to each of the appropriate fuzzy sets via membership functions (Fig. 97.3).
97.3.1.2 Rule Base Evaluation The rules are expressed in conventional antecedent-consequent form. The type of fuzzy Interface System used is ‘Mamdani’. Mamdani is one of the pioneers in the application of fuzzy logic in control system. This is the most commonly used implication method [9]. The two input variables have their own membership
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Fig. 97.3 Fuzzy membership functions
functions. The rules are evaluated using IF-TEHN rules. The flow chart of the rules is shown in Fig. 97.4. Totally of 64 rules has been has been developed. Some of the samples are shown below 1. IF Wind Direction is WVLA (Wind Very Low Angle) AND Turbine Head is TLMA (Turbine Low Medium Angle) THEN Torque is ACT (Anti Clockwise Torque) 2. IF Wind Direction is WLA (Wind Low Angle) AND Turbine Head is TLHA (Turbine Large High Angle) THEN Torque is CT (Clockwise Torque) 3. IF Wind Direction is WLMA (Wind Low Medium Angle) AND Turbine Head is TLMA (Turbine Low Medium Angle) THEN Torque is ZT (Zero Torque) 4. IF Wind Direction is WHMA (Wind High Medium Angle) AND Turbine Head is TVLA (Turbine Very Low Angle) THEN Torque is CT (Clockwise Torque) 5. IF Wind Direction is WHA (Wind High Angle) AND Turbine Head is TLA (Turbine Low Angle) THEN Torque is ACT The rules are evaluated with AND (min) operation of the fuzzy operation set. The Degree of Fulfillment (DOF) of the rule is DOF1 ¼ lWVLA ð X Þ ^ lTLMA ðY Þ
ð97:3Þ
V where is minimum operator and lWVLA and lTLMA are the membership functions of Wind Direction (X) and Turbine Head (Y), respectively. The DOF of the remaining rules are DOF2 ¼ lWLA ð X Þl^TLHA ðYÞ
ð97:4Þ
DOF3 ¼ lWLMA ð X Þl^TLMA ðYÞ
ð97:5Þ
DOF4 ¼ lWHMA ð X Þl^TVLA ðYÞ
ð97:6Þ
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Fig. 97.4 Flow chart
DOF5 ¼ lWHA ð X Þl^TLA ðYÞ
ð97:7Þ
The rule output is given by the truncated membership function. The total fuzzy output is the union (OR) of all the component membership functions. lOUT ðZ Þ ¼ lWVLA0 ðZ Þ_lTLMA0 ðZ Þ_lWLA0 . . .lWLMA0 ðZ Þ
ð97:8Þ
where _ is maximum operator and lx0 are the membership functions for total fuzzy output. It is the union of all component membership function of the universe.
97.3.1.3 Defuzzification As the fuzzy number is determined by the membership function, to achieve the purpose of ranking, the sort of fuzzy numbers is to construct various order
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Fig. 97.5 Torque output
relationships from the standpoint of membership function to some extent. Practically, the centroids of fuzzy numbers are some properties of fuzzy numbers which are extracted from geometric aspects [10]. The centroid method is used here for defuzzifing fuzzy output functions. This procedure (also called center of area, center of gravity) is the most prevalent and physically appealing of all the defuzzification methods [11, 12], it is given by the algebraic expression R l ðzÞ:zdz Z ¼ R c lc ðzÞ:dz
ð97:9Þ
R where denotes an algebraic integration. The calculation of defizzified value using centroid method is converted into analog data.
97.4 Simulation Result The two inputs parameters are given as wind direction and nacelle position as a signal to fuzzy controller. As the two inputs are same the torque produced should be zero. That is, as the missile is pointing in the direction of wind there is no torque (Zero) needed rotate the missile. If the direction of wind changes from the present
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Fig. 97.6 Surface view
position the error is created, can be compensated using fuzzy controller and makes the nacelle to rotate in clockwise or anticlockwise direction (Fig. 97.5). A detailed description of the control systems for both the converters and the yaw angle command can be found in reference MathWorks [13]. The Fuzzy Logic Toolbox allows you to do several things, but the most important thing it lets you do is create and edit fuzzy inference systems. You can create these systems by hand, using graphical tools or command-line functions, or you can generate them automatically using either clustering or adaptive neuro-fuzzy techniques. The fuzzy toolbox is used to develop this controller for the wind power plant. The three dimensional representation of the output is surface viewer (Fig. 97.6).
97.5 Conclusion A wind turbines yaw control using fuzzy controller is modeled and simulated. The system components namely, the wind turbine, yaw system and fuzzy controller have been modeled and brought together for wind energy conversion. Control of overall yaw system has been achieved by fuzzy logic controller. The high quality extracted power from the wind and electrical energy implies that the fuzzy controller has reflected its advantages to the system. Tracking the wind direction and regulating the power output is achieved by yaw system control. The positive or negative torque of the yaw motors drives the yaw ring gears towards the direction of maximum wind speed. The presented simulation results demonstrate that the proposed yaw control is feasible and has certain advantages.
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