Essay: Sun and Maximum Power Point Tracking in Solar Array Systems Using Fuzzy Controllers

Abstract: Solar energy is viewed as clean and renewable source of energy for the future. So the use of Photovoltaic systems has increased in many applications. That need to improve the materials and methods used to harness this power source. There are two major approaches; sun tracking and maximum power point tracking. These two methods need efficient controllers. The controller may be conventional or intelligent such as Fuzzy Logic Controller (FLC). FLCs have the advantage to be relatively simple to design as they do not require the knowledge of the exact model and work well for nonlinear system. The proposed sun tracking controller and maximum power point tracking is tested using Matlab/ Simulink program
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Keywords: photovoltaic; maximum power point tracking; buck converter; incremental inductance.
I. Introduction:
Renewable energy sources play an important role in electric power generation. There are various renewable sources which used for electric power generation, such as solar energy,wind energy, geothermal etc. Solar Energy is a good choice for electric power generation,since the solar energy is directly converted into electrical energy by solar photovoltaic modules. These modules are made up of silicon cells. When many such cells are connected in series we get a solar PV module. The current rating of the modules increases when the area of the individual cells is increased, and vice versa. When many such PV modules are connectedin series and parallel combinations we get a solar PV arrays, that suitable for obtaining higher power output.
The applications for solar energy are increased, and that need to improve the materials and methods used to harness this power source. Main factors that affect the efficiency of the collection process are solar cell efficiency, intensity of source radiation and storage techniques. The efficiency of a solar cell is limited by materials used in solar cell manufacturing. It is particularly difficult to make considerable improvements in the performance of the cell, and hence restricts the efficiency of the overall collection process. Therefore, the increase of the intensity of radiation received from the sun is the most attainable method of improving the performance of solar power. There
are three major approaches for maximizing power extraction in solar systems. They are sun tracking, maximum power point[1],[2] (MPP) tracking or both. These methods needs controllers, may be intelligent such as fuzzy logic controller or conventional controller such as PID controller. The advantage of the fuzzy logic control is that it does not strictly need any mathematical model of the plant. It is based on plant operator experience, and it is very easy to apply. Hence, many complex systems can be controlled without knowing the exact mathematical model of the plant. In addition, fuzzy logic simplifies dealing with nonlinearities in systems. The nice thing about fuzzy logic control is that the linguistic system definition becomes the Control algorithm.
II. Methodology
The major things that have to be considered in development of sun and MPPT tracker are designing of PV module, DC to DC buck converter, MPPT algorithm and sun tracker.
A. PV modelling:
The PV module characteristic is a non linear characteristic which depends on the reverse saturation current of the diode in parallel to the photo current source. The photon current is produced because of the photovoltaic action [3],[4]. The equivalent circuit of a PV cell is as depicted in Figure 1. The current source Iph represents the cell photocurrent. The series and shunt resistances are represented as Rs and Rsh. The PV cells are grouped in larger units called PV modules which are further interconnected in a parallel-series configuration to form PV arraysThe current source Iph represents the cell photocurrent. Rsh and Rs are the intrinsic shunt and series resistances of the cell, respectively. Usually the value of Rsh is very large and that of Rs is very small, hence they may be neglected to simplify the analysis. The PV cells are grouped in larger units called PV modules which are further interconnected in a parallel-series configuration to form PV arrays.
Fig 1. PV Cell Modelled as Diode Circuit
The photovoltaic panel can be modeled mathematically as given in equations (1) – (4)
Module photo-current:
‘ I’_ph= [‘ I’_scr+ ‘ k’_i (T ‘ 298)]*?? /1000 (1)
Where ‘ I’_ph is the photocurrent in (A) which is the light-generated current at the nominal condition (25’ and 1000W/m2), Ki is the short-circuit current/temperature coefficient ‘ I’_scr at (0.0017A/K), T is the actual temperature in K, ?? is the irradiation on the device surface, and 1000W/m2 is the nominal irradiation.
Modules reverse saturation current’ I’_rs:
‘ I’_rs=’ I’_rs/[exp(q’ v’_oc/NskAT)’1] (2)
The module saturation current I_Ovaries with the cell temperature, which is given by:
I_O=I_rs [ T/T_r ]^3exp[‘q*E’_go/BK{1/Tr-1/T}] (3)
Where I_O is the diode saturation current (A). The basic equation that describes the current output of the photovoltaic (PV) module I_PV of the single-diode model is as given in equation.
Thus,
I_(PV=) N_P ‘*I’_PH-N_P*I_O[exp {‘((q*(V_PV+I_PV Rs))/(N_s AKT))}-1] (4)
Where K is the Boltzmann constant (1.38 x ’10’^(-23) JK^(-1)), q is the electronic charge (1.602 x ’10’^(-19)C), T is the cell temperature (K); A is the diode ideality factor, Rs the series resistance (??). Ns is the number of cells connected in series. Np is the number of cells connected in parallel, V_PV=V_oc.These reference values are generally provided by manufacturers of PV modules for specified operating condition such as STC (Standard Test Conditions) for which the irradiance is 1000 W/m2 and the cell temperature is 25’. The panel specifications are used to design the simulink module of the required panel. The major factors are the open circuit voltage and the short circuit current.
B. DC TO DC CONVERTER:
A buck converter [5],[6]is a step-down DC to DC converter. The operation of the buck converter is fairly simple, with an inductor and two switches (transistor and diode) that control the current of the inductor as shown in Figure.
Fig 2. DC to DC to buck converter
It alternates between connecting the inductor to source voltage to store energy in the inductor when the PWM signal is high and discharging the inductor into the load when the PWM signal is low. When the duty cycle is in ON state, The diode become as reversed biased and the inductor will deliver current and switch conducts inductor current. The current through the inductor increase, as the source voltage would be greater. The energy stored in inductor increased when the current increase, and the inductor acquires energy. Capacitor will provides smooth out of inductor current changes into a stable voltage at output voltage. When the duty cycle is in OFF state, The diode is ON and the inductor will maintains current to load. Because of inductive energy storage, inductor current will continues to flow. While inductor releases current storage, it will flow to the load and provides voltage to the circuit. The diode is forward biased. The current flow through the diode which is inductor voltage is equal with negative output voltage.
C)FUZZY logic controller:
Fuzzy Logic is a control system methodology designed for solving problems
implemented in a widely range of systems such as: simple and small devices, microcontrollers or, on the other hand, large systems joined to networks, workstations or normal control systems. It may be developed in hardware, software, or both in combination.
Fuzzy logic extends conventional Boolean logic to handle the concept of the partial truth the values falling between ‘totally true’ and ‘totally false’. These values are dealt with using degree of membership of an element to a set. The degree of membership can take any real value in the interval [0, 1]. Fuzzy logic makes it possible to imitate the behavior of human logic, which tends to work with ‘fuzzy’ concepts of truth.
Fuzzy Logic shows a usual rule-based IF condition AND condition THEN action. It
approaches to a solving control problem rather than intending to model a system based on math’s. The Fuzzy Logic model is based on empiric experience, relying on a number of samples rather than some technical understanding of the problem to be solved.
The basic parts of every fuzzy controller are displayed in the following Figure .The fuzzy logic controller (FLC) is composed of a fuzzification interface, knowledge base, inference engine, and defuzzification
The fuzzifier maps the input crisp numbers into the fuzzy sets to obtain degrees of membership. It is needed in order to activate rules, which are in terms of the linguistic variables. The inference engine of the FLC maps the antecedent fuzzy (IF part) sets into consequent fuzzy sets (THEN part). This engine handles the way in which the rules are combined. The defuzzifier maps output fuzzy sets into a crisp number, which becomes the output of the FLC.
Fig 3: Fuzzy Logic Controller
D) Sun tracker
Solar tracking system uses a stepper motor as the drive source to rotate the solar panel. The position of the sun is determined by using a tracking sensor, the sensor reading is converted from analog to digital signal, then it passed to a fuzzy logic controller. The controller output is connected to the driver of the stepper motor to rotate PV panel in one axis until it faces the sun[9]-[13].
FLC has two inputs which are: error and the change in error, and one output feeding to the stepper motor driver. The phasor plot in Figure 5.11 explained a method used in reaching the desired degree value at the equilibrium point to satisfy the stability in the system. For example, at stage A the error is positive (desired degree ‘actual degree) and the change error (error ‘ last error) is negative which means that the response is going in the right direction;
hence, the FLC will go forward in this direction. Using the same criteria at stage B, the error is negative and CE is bigger negative; hence, the response is going in wrong direction so FLC will change its direction to enter Stage C, until reaching the desired degree.
Fig 4: FLC working phenomenon
E) MPPT fuzzy logic controller
The FLC examines the output PV power at each sample (time_k), and determines the change in power relative to voltage (dp/dv). If this value is greater than zero the controller change the duty cycle of the pulse width modulation (PWM) to increase the voltage until the power is maximum or the value (dp/dv)=0, if this value less than zero the controller changes the duty cycle of the PWM to decrease the voltage until the power is maximum as shown in Figure 5:
Fig 5: V-I graph for a Photo voltaic module
FLC has two inputs which are: error and the change in error, and one output feeding to the pulse width modulation to control the DC-to-DC converter [7]-[9]. The two FLC input variables error E and change of error CE at sampled times k defined by:
(5)
Change _Error (k ) ”Error (k ) ‘Error (k ‘1)
where P(k ) is the instant power of the photovoltaic generator. The input error (k) shows if the load operation point at the instant k is located on the left or on the right of the maximum power point on the PV characteristic, while the input CE (k) expresses the moving direction of this point. The fuzzy inference is carried out by using Mamdani method, FLC for the Maximum power point tracker
III SIMULATION AND RESULTS:
A: SOLAR TRACKING SYSTEM
Figure 6 the Simulink block diagram for the Fuzzy controller for sun tracker system.
The controller has been tested using Simulink motor module in MATLAB, by applying the step input and initial degree of the rotor is -10 degree. The output step response is shown in Figures 5.17. The range from -10 to 0 degree takes 5 steps since each step in our motor is 1.8 degree, so (10/1.8)= 5 stepsThe fuzzy logic in the above block diagram shows the diagram consisting of error and change of error as input and output. The membership functions of input and output are shown in the below diagram.
(a)
(b)
( c)
Fig 7(a),(b),(c) input and output membership functions for sun tracking FLC
fig 8: stepper motor angle rotation for tracking system
Fig 9: zoom for one motor step
B: SOLAR MPPT SYSTEM:
Fig 10: controlling PV power using FLC
Fig 11: generating error and change in error
(a)
(b)
(c)
Fig 12(a),(b) ,(c) input and output membership functions of MPPT FLC
Table 1. Fuzzy Rules Base
de
e NB NM NS ZE PS PM PB
NB NB NB NB NB NM NS ZE
NM NB NB NB NM NS ZE PS
NS NB NB NM NS ZE PS PM
ZE NB NM NS ZE PS PM PB
PS NM NS ZE PS PM PB PB
PM NS ZE PS PM PB PB PB
PB ZE PS PM PB PB PB PB
Fig 13: Fuzzy MPPT Versus P&O MPPT power
CONCLUSION:
In this thesis, fuzzy logic controller FLC was designed to maximizing the energy received from solar cells by two methods.
The first method is by implementing a sun tracker controlled by fuzzy logic controller to keep the PV panel pointing toward the sun by using a stepper motor. The use of stepper motor enables accurate tracking of the sun This controller has been tested using Matlab/Simulink program. The other proposed method is by implementing a maximum power point tracker controlled by fuzzy logic controller and using buck DC-to-DC converter to keep the PV output power at the maximum point all the time. This controller was tested using Matlab/Simulink program, and the results was compared with a perturbation and observation controller applied on the same system. The comparison show that the fuzzy logic controller was better in response and don’t depend on knowing any parameter of PV panel.
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