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CONTROL METHOD OF PHOTOVOLTAIC MAXIMUM POWER POINT TRACKING BASED ON FUZZY THEORY 1 Priyank Srivastava, 2Saumitra Mishra, 3Pankaj Gupta 1Assistant Professor, EN Dept, SMS Technical Campus, Lucknow, India 2Sr. Lecturer, EC Dept, SRMCEM, Lucknow, India 3Assistant Professor, EN Dept, SMS Technical Campus, Lucknow, India due to other factors such as PV module temperature and load conditions. In order to maximize the power derived from the PV module it is important to operate the module at its optimal power point. To achieve this, a controller called a Maximum Power Point Tracker is required. The feedback control of maximum power point tracking is usually based on power as a variable, which play the role of the photovoltaic cell internal resistance and external load impedance matching. The fuzzy control is a typical intelligent control method, its most important feature is the experience and knowledge of the experts used to control the language rules, it does not rely on the precise mathematical model of controlled object and be able to overcome the nonlinear effects. In the photovoltaic power generation system, we usually via a DC / DC or DC / AC connected photovoltaic arrays and load, adjust the PWM duty cycle to achieve optimal matching of the damping, so that maximum output power can be achieved. Fuzzy control can automatically adjust the duty cycle based on the magnitude of the power change, quick to perceive the external environment changes, in the case that does not interfere with the normal work of the system, find the maximum power point. Thus, the adaptive duty cycle disturbances MPPT method based on fuzzy logic control is proposed. Abstract— There is a large energy demand due to industrial development and population growth specially in India. The main challenge in replacing conventional energy sources with newer and more environmentally friendly alternatives, such as solar and wind energy, is how to capture the maximum energy and deliver the maximum power at a minimum cost for a given load. The output power of Photovoltaic cells or solar panels has nonlinear characteristics and these are affected by temperature, light intensity and load. In order to improve photoelectric conversion efficiency, maximum power point tracking (MPPT) is required. According to the mechanism and control methods of Maximum Power Point Tracking (MPPT), a new modified adaptive duty cycle perturbation MPPT control method which is based on fuzzy theory is presented. The experimental results show the proposed system can track the MPP with better performance characteristics. Index Terms— Buck-Boost Converter, Fuzzy Logic Controller (FLC), Maximum Power Point Tracking (MPPT), Photovoltaic (PV) Introduction Among various renewable and sustainable energy sources, solar energy provides the opportunity to generate power without emitting any greenhouse gas. The photovoltaic (PV) system technologies have increasing roles in electric power technologies, providing more secure power sources and pollution-free electric supplies. Photovoltaic power generation system can directly convert solar energy into electrical energy. The current - voltage output of photovoltaic battery is nonlinear, coupled with changes in the sunshine, temperature and other factors, the output power is constantly changing. The PV array can supply the maximum power to the load at a particular operating point which is generally called as maximum power point (MPP), at which the entire PV system operates with maximum efficiency and produces its maximum power. Currently, PV modules have very low efficiencies with only about 12 − 29% efficiency in their ability to convert sunlight to electrical power. Gallium Arsenide solar cells have a high efficiency of 29%, while Silicon solar cells have an efficiency of about 12-14%. The efficiency can drop further Photovoltaic Cell Characteristics A PV module is composed of individual solar cells connected in series and parallel and mounted on a single panel. The goal is to calculate the power output from a PV module based on an analytical model that defines the current-voltage relationships based on the electrical characteristics of the module. Photovoltaic Cell Mathematical Model and the Output Characteristics. According to the electronic theory, the equivalent mathematical model of the photovoltaic cells is: I = Isc – I0[ exp[ (V + IRS)/AKT ] - 1] – [(V + IRS)/RSH] (1) Where IPV = [Ipvr + KI( T-298)]S/100 (2) 1 (3) FIG.1. EQUIVALENT CIRCUIT OF A SOLAR CELL Power-Voltage characteristics Where I and V is respectively the battery output current and port voltage; IPV is the photocurrent; I0 is the battery internal equivalent diode PN junction reverse saturation current; q is the charge of an electron; A is diode PN junction ideality factor; K is the Boltzmann constant, 1. 38 ×10-23 J / K ; T is the operating temperature of photovoltaic cells; Rs is the battery's series resistance; T r is the absolute temperature, 273K; Ior is the diode saturation drain current under Tr; Eco is the width of silicon; S is the light intensity, W / m2; Ipvr is the short-circuit current under the conditions of 298 K and of 1000 W/ m2 ; KI is short-circuit current temperature coefficient under the conditions of I scr. Affected by environmental factors such as light, temperature, the output characteristics of photovoltaic cells has the obvious nonlinear. Based on the above model can make the IV and PV curves of photovoltaic cells are as shown in Figure1. FIG. 2. THE OUTPUT CHARACTERISTICS OF PHOTOVOLTAIC CELLS Basic Principles of Fuzzy Control Fuzzy Logic Fuzzy Logic is a branch of Artificial Intelligence. It owes its origin to Lofti Zadeh, a professor at the University of California, Berkley, who developed fuzzy set theory in 1965. The basic concept underlying fuzzy logic is that of a linguistic variable, that is, a variable whose values are words rather than numbers (such as small and large). Fuzzy logic uses fuzzy sets to relate classes of objects with unclearly defined boundaries in which membership is a matter of degree[20]. Fuzzy Sets and Membership Functions A fuzzy set is an extension of a crisp set where an element can only belong to a set (full membership) or not belong at all (no membership). Fuzzy sets allow partial membership which means that an element may partially belong to more than one set. A fuzzy set A is characterized by a membership function µA that assigns to each object in a given class a grade of membership to the set. The grade of membership ranges from 0 (no membership) to 1 (full membership) written as, µA :U [0,1] which means that the fuzzy set A belongs to the universal set U (called the universe of discourse) defined in a specific problem. A membership function defines how each point in the input space is mapped to a degree of membership[20]. Fuzzy Rules and Inferencing The use of fuzzy sets allows the characterization of the system behavior through fuzzy rules between linguistic variables. A fuzzy rule is a conditional statement Ri based on expert knowledge expressed in the form: Ri: IF x is small THEN y is large Current-Voltage characteristics 2 where x and y are fuzzy variables and small and large are labels of the fuzzy sets. If there are n rules, the rule set is represented by the union of these rules i.e., R = R1 else R2 else ….. Rn . A fuzzy controller is based on a collection R, of control rules. The execution of these rules is governed by the compositional rule of inference. Fuzzy Controller Structure The general structure of a fuzzy logic controller is presented in Figure and comprises of four principal components: • Fuzzification interface: - It converts input data into suitable linguistic values using a membership function. • Knowledge base: - Consists of a database with the necessary linguistic definitions and the control rule set. • Inference engine: - It simulates a human decision making process in order to infer the fuzzy control action from the knowledge of the control rules and the linguistic variable definitions. • Defuzzification interface: - Converts an inferred fuzzy controller output into a non-fuzzy control action. Fig. 4: Current-voltage characteristic of a PV module Fig. 5: Power-voltage characteristic of a PV module Figure 3 Basic configuration of fuzzy logic controller[20] Fig 6: Photovoltaic MPPT system PRINCIPLE OF MAXIMUM POWER POINT TRACKING CONTROL The photovoltaic module operation depends strongly on the load characteristics, (Fig. 4 and 5) to which it is connected [14,]. Indeed, for a load, with an internal resistance R i , the optimal adaptation occurs only at one particular operating point, called Maximum Power Point (MPP) and noted in our case P max . Thus, when a direct connection is carried out between the source and the load, (Fig. 4), the output of the PV module is seldom maximum and the operating point is not optimal. To overcome this problem, it is necessary to add an adaptation device, MPPT controller with a DC-DC converter, between the source and the load, (Fig. 6), [14]. Furthermore the characteristics of a PV system vary with temperature and insolation, (Fig. 7 and 8) [6, 7]. So, the MPPT controller is also required to track the new modified maximum power point in its corresponding curve whenever temperature and/or insolation variation occurs[14]. Fig. 7: Influence of the solar radiation for constant temperature 3 • Any sensor data that provides some indication of a system's actions and reactions is sufficient. This allows the sensors to be inexpensive and imprecise thus keeping the overall system cost and complexity low. • Its rule-based operation enables any reasonable number of inputs to be processed and numerous outputs generated. The control system can be broken into smaller units that use several smaller fuzzy logic controllers distributed on the system, each with more limited responsibilities.[20] MODELING AND SIMULINK RESULTS Buck-boost converter modelling The model of a buck-boost converter is shown in Figure 6.4 . It consists of four basic components; Switching device (MOSFET), diode, inductor, and capacitor. These components are not ideal and some of the non-idealities are considered when modeling. The inductor is modeled as an ideal inductor in series with a resistance L R. The capacitor is modeled as an ideal capacitor in series with a resistance C R. L R and C R are used to model the power losses in the inductor and capacitor respectively. Step-up and Step-down of voltages depends upon the values of Duty cycle D Fig. 8: Influence of the temperature of junction for constant insolation Application of Fuzzy Logic in MPPT Control If the value of duty cycle is less than 0.5 then step down occurs and above 0.5 step up occurs –For D = 1/2, the output voltage is the same as the input voltage. –For D = 2/3, the output voltage is double the input voltage, –For D = 1/3, the output voltage is half the input voltage Fig. 9. Flowchart of the algorithm steps Fuzzy logic control relies on basic physical properties of the system, and it potentially able to extend control capability even to those operating conditions where linear control techniques fail, i.e., large signal dynamics and large parameter variations. As fuzzy logic control is based on heuristic rules, application of nonlinear control laws to overcome the nonlinear nature of dc-dc converters is easy. Fuzzy logic offers several unique features that make it a particularly good choice for these types of control problems because: • It is inherently robust as it does not require precise, noisefree inputs. The output control is a smooth control function despite a wide range of input variations. • It can be easily modified to improve system performance by generating appropriate governing rules. Fig.10 Model simulation of BUCK-BOOST COVERTER in MATLAB/SIMULINK 4 Result analysis for buck-boost 40 300 35 250 30 200 OUTPUT VOLTAGE OUTPUT VOLTAGE 25 20 15 150 100 10 50 5 0 0 -5 0 1 2 3 4 5 TIME 6 7 8 9 10 -50 0 1 2 3 4 5 TIME 6 7 8 9 10 Fig. 11.Output of buck-boost converter when input voltage is 100V and duty cycle is 0.2 Fig.12 Output of buck-boost converter when input voltage is 100V and duty cycle is 0.7 MPPT Fuzzy Logic Controller MODELLING & RESULT Fuzzy Logic (FL) is one of the most popular control methods which is known by its multi-rule-based variable’s consideration . This method provides faster results compared to other Artificial Intelligent control methods such as Genetic Algorithm and Neural Networks. Being fast and robust are the main reasons of choosing FL for MPPT in the current study. To implement the FL in a problem, different steps of this algorithm must be taken which are as follows. Fig.13 Modeling of fuzzy logic controller value of . The max-min method was applied to extract the from the triangle type membership function. Fuzzification The input defined in Equations F(t) = 𝛥𝑃(𝑡) 𝛥𝑉(𝑡) = 𝑃(𝑡)−𝑃(𝑡−1) 𝑉(𝑡)−𝑉(𝑡−1) and ΔF(t) = F(t) − F(t − 1) need to be fuzzified by some membership functions. For each input value, the respective membership function returns a Inference Diagram A rule base must be applied to the obtained membership function according to Mamdani. The rule table is designed and shown in Table1. 5 ΔF(t) F(t) NB NB NM NS ZE PS PM PB ZE ZE ZE NB NB NB NB NM NS ZE PS PM PB ZE ZE NS PS PM PB ZE ZE ZE PS PM PB ZE ZE ZE PS PM PB NM NS ZE PS PM PB NM NS ZE ZE ZE ZE NM NS ZE ZE ZE ZE NM NS PS ZE ZE ZE The 3D input-output surface is also obtained by using MATLAB FL Inference. Table 1: control rules of fuzzy logic controller Now modeling of MPPT fuzzy controller for its each membership function inputs and outputs are shown in the following figures. Fig.15. Surface waveform of the FLC Defuzzification For the Defuzzification, the centroid method is applied to return a proper value for the duty cycle variation (ΔD). The difuzzified output value of the FLC must be added to a reference value of duty cycle which is considered equal to 0.5 for the current study. The result is the optimum value of D can be sent to the buck-boost converter as a control signal Thus variations in duty cycle have been implemented with the help of fuzzy logic controller for various input combinations under different conditions of variations. (a) CONCLUSION On the whole, it is concluded that the overall objective of formulating and implementing a fuzzy logic controller for maximum power point tracker of a photovoltaic power system has been met. All membership functions of Fuzzy logic controller were modeled in MATLAB. Surface Waveform for Fuzzy controller was plotted and seen. Fuzzy rules were implemented for more smooth operation. Rule viewer fuzzy controller results were achieved for duty cycle adjustment i.e. fuzzy controller output. Current-Voltage characteristics curves for photovoltaic are successfully plotted and seen. Also Power-Voltage characteristics curve are successfully plotted and seen. Buck-boost converter was found to be the most suitable topology among Buck, Boost and Buck-Boost converter for MPP tracking. Buck-Boost converter is successfully modeled. Its step-up and step-down operations are successfully achieved and results are shown. Although there is a large amount of work that can and should still be done, the work in this paper has created a solid foundation to allow that work to continue. (b) (c) Fig. 14 Membership function of (a) input F, (b) input ΔF and (c) output ΔD 6 [15] Mekhilef S., Saidur R. and Safari A., “A Review of Solar Energy use in Industries”, Elsevier Renewable and Sustainable Energy Reviews, Vol. 15, pp. 1777-1790, 2011. [16]. 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