CHAPTER 4 FUZZY LOGIC BASED PHOTO VOLTAIC ENERGY SYSTEM USING SEPIC

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1 56 CHAPTER 4 FUZZY LOGIC BASED PHOTO VOLTAIC ENERGY SYSTEM USING SEPIC 4.1 INTRODUCTION A photovoltaic system is a one type of solar energy system which is designed to supply electricity by using of Photo Voltaic (PV) cell. It consists of an arrangement of several components, including solar panels to absorb and directly convert sunlight into electricity, a DC-DC converter and inverter to change from DC to AC. It may also use a solar tracking system to improve the overall performance of the system and integrated battery control. Photo Voltaic (PV) is a method of generating electrical power by converting sunlight into direct current electricity using semiconductor materials that exhibit the photovoltaic effect. A photo voltaic system employs solar panels composed of a number of solar cells to supply usable solar power. Power generation from solar PV has long been seen as a clean, sustainable energy technology, which draws upon the planet s most plentiful and widely distributed renewable energy source The sun. The direct conversion of sunlight to electricity occurs without any moving parts or environmental emissions during operation are the best advantage of the PV energy conversion system. It is well proven that photovoltaic systems have been used for fifty years in specialized applications, and grid-connected PV systems.

2 57 Maximum Power Point Tracking (MPPT) is a technique. The grid connected converter, inverters, solar battery chargers and similar devices use to get the maximum possible power from one or more photovoltaic device. Solar cells have a complex relationship between solar irradiation, temperature and total resistance that produces a non-linear output efficiency Esram & Chapman (2007) have discussed many different techniques for MPPT of PV. They have discussed nearly 19 distinct methods related to MPPT techniques. Viswanathan et al (2005) have used FLC for power converters to approximate the nonlinear functional controller. They have used in the PI-FLC. The results of PI-FLC are replaced by a simple Nonlinear PI controller to obtain a good dynamic performance in power converters and tested with the boost converter. A novel design procedure of a PI like FLC for DC-DC converters have been presented by Perry et al (2007). They designed integrated linear control technique with fuzzy logic. As a result, a non linear controller provides improved performance over the linear PI controller. A dimmable high power factor compact fluorescent lamp electronic ballast has been explained by Lam et al (2012). The ballast is compatible with standard incandescent lamp dimmers. The ballast power circuit is a single switch single stage resonant inverter which is essentially a fusion of a SEPIC converter. Elmas et al (2009) have introduced a complete design method to construct an adaptive fuzzy logic controller for DC-DC converter. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. Compared to traditional binary sets, fuzzy logic variables may have a true value that ranges in degree between 0 and 1. When linguistic variables are used, these degrees may be managed by specific functions. While variables in mathematics usually take numerical

3 58 values, in fuzzy logic applications, the non-numerics are often used to facilitate the expression of rules and facts. The advantages of fuzzy logic controller are its ability to deal with nonlinearities and uncertainties. The fuzzy control is that it is also applicable for multivariable system, whereas PIs are useful only for linear systems. Fuzzy logic focuses on fixed and approximate reasoning. A variable in fuzzy logic can take a true value range between 0 and 1, as opposed to take true or false in traditional binary sets. Increasing energy demand and environmental issues over the fossil fuels have significantly developed an interest in green energy sources to replace the fossil fuel. The PV power systems are gaining more popularity than other renewable sources because of their ease of installation, less maintenance, etc. Tse et al (2002) have used incremental conductance technique for MPPT of solar energy without using sophisticated digital sampling or mathematical manipulations. A SEPIC converter is used to provide a constant DC bus voltage and duty cycle has been controlled by the MPPT controller. The modified incremental conductance approach has been utilized for MPPT. In this approach, MPPT controller has automatically generated a PWM signal for the SEPIC converter to extract maximum power as discussed by Adhikari et al (2011). A two loop digital control strategy has been proposed for the two input integrated converters and then, the optimal power distribution has been realized in the input DC sources. This can be achieved by using two decoupled digital controllers, one for load voltage regulation and another for Low Voltage Side (LVS) current control as discussed by Kottas et al (2006). A low power stand-alone solar PV energy generation system with a SEPIC converter has been designed. The performance analysis of the system

4 59 has been presented under variation in solar radiations with the device current and voltages by veerachary et al (2009). Single switch cell voltage equalizers by using multi staked buck-boost converter for series connected energy storage cell have been used by Wu et al (2000). A low power, solar PV energy generation system with the utilization of a MPPT controller and a SEPIC converter are developed in this work. A fuzzy logic controller generates the PWM signal for the SEPIC converter to extract maximum power. The system is designed with 750W solar panel to feed an average load demand of 250W. The excess energy obtained from the PV generating system after meeting the load demand is stored in a battery storage system. The SEPIC converter with MPPT controller is needed to improve PV panel efficiency and matches the load to the photovoltaic module. 4.2 FUZZY LOGIC CONTROLLER The limitation of the PI controller that it is useful only for linear systems. The SEPIC converter is linearized at a certain operating condition and a particular PI controller is designed for it. But to improve the SEPIC for multi variable operating conditions, fuzzy logic controller can be used. The advantage of fuzzy logic controller is that its ability to deal with nonlinearities and uncertainties. It is robust and adaptive control. This is a very important issue of two fold, on one hand, research part which deals with the improvement of controller design of multivariable, nonlinear, unstable, time delay with uncertainties systems and on the other hand, industrial applications where the main objective is to obtain practical solutions with low cost of their problems nonlinear systems. Fuzzy logic provides a method to make definite decisions based on imprecise and ambiguous input data. Fuzzy

5 60 logic is widely used for applications in control systems, since it closely resembles how a human make a decision but in a faster way. Fuzzy logic can be incorporated in control systems based on small handheld devices to large PC workstations. Fuzzy logic allows to make a definite decision based on imprecise or ambiguous data, whereas ANN tries to incorporate human thinking process to solve problems without mathematically modeling them. Even though both of these methods can be used to solve nonlinear problems, and problems that are not properly specified, they are not related PV System The PV system directly converts sunlight into electricity. A SEPIC converter is used to process the electricity from the PV system. This SEPIC converter either increase or decrease the PV system voltage to the load. The proposed SEPIC converter operates in boost mode because the output voltage required more than the input. Figure 4.1 Equivalent circuit of a PV cell The practical equivalent circuit of a PV module is shown in Figure 4.1 and its typical output characteristics are shown in Figures 4.2 and 4.3. In the equivalent circuit, the current source represents the current generated by the light photons and its output is constant under constant

6 61 temperature and constant irradiance. The diode shunted with the current source determines the I-V characteristics of PV module. Here, the resistance in series with the current path through the semiconductor material, the metal grid, contacts, and a current collecting bus is lumped together as a series resistor R s. Its effect becomes very noteworthy in a PV module. The loss associated with a small leakage of current through a resistive path in parallel with intrinsic device is represented by a parallel resistor R p. Its effect is much less noteworthy in a PV module compared to the series resistance and it will only become noticeable when a number of PV modules are connected in parallel for a larger system. From Figure 4.1, the characteristics equation of equivalent solar cell can be expressed as (4.1). ( V + I R ) q I Pv = I ph I r exp pv pv s 1 (4.1) AKT where I pv is the output current from the PV array, V pv is the output voltage of the PV array, I ph is the photo generated current of the PV cell, I r is the reverse saturation current of the diode D, q is the electronic charge, A is the ideality factor, K is the Boltzmann constant, T is the operating temperature of the cell and Rs is the internal resistance of the cell. From the Equation (4.1), the PV cell is modeled in Simulink PV array current (A) PV array Voltage(V) Figure 4.2 Voltage and current characteristics of PV

7 PV array Power (W) PV array Voltage (V) Figure 4.3 Power and voltage characteristics of PV As seen in the power versus voltage curve of the module shown in the Figure 4.3, there is a single maximum of power and there exists a peak power corresponding to a particular voltage and current. Since the module efficiency is low, it is desirable to operate the module at the peak power point. Hence, the maximum power can be delivered to the load under varying temperature and insulation conditions. Hence, maximization of power improves the utilization of the solar PV module. A MPPT is used for extracting the maximum power from the solar PV module and transfers it to the load. Table 4.1 shows the PV Module Specifications which are used in this work. Table 4.1 PV Module Specifications Sl. No Specification Value 1 Maximum Power (P max ) 750W 2 Voltage at Pmax (V mp ) 106.5V 3 Current at Pmax (I mp ) 7.04A 4 Open circuit voltage (V oc ) V 5 Short circuit current (I sc ) 7.63A

8 PROPOSED CONTROLLER Fuzzy Logic control Fuzzy logic control is used in this work. The term fuzzy refers to the fact that the logic involved can deal with concepts that cannot be expressed as the true or false but rather as partially true. Fuzzy logic has the advantage of the solution to the problem which can be cast in terms that human operators can understand. Hence, that experience can be used in the design of the controller Stand-alone Solar PV System A solar photovoltaic (PV) system is made up of solar PV panels, converter, an inverter, etc. Solar PV systems work very simply to provide a household or commercial structure with usable, renewable, clean and green solar power. The inverter then changes the DC into 230 volts alternating current (AC) electricity, which can be used for a household's electrical needs such as lighting and electrical appliances. Stand alone solar Photovoltaic (PV) system with the isolated SEPIC converter is proposed in this work with the fuzzy logic controller Isolated Inductors Two inductors or coils that are linked by electromagnetic induction are said to be coupled inductors. The phenomenon of one inductor inducing a voltage in another inductor is known as mutual inductance. Coupled coils can be used as a basic model for transformers, an important part of power distribution systems and electronic circuits. Transformers are used for changing alternating voltages, currents, impedances, and to isolate one part of a circuit from another. In this work, the isolated

9 64 SEPIC converter is used in order to separate source and load using this coupled inductors. Figure 4.4 Block diagram of Fuzzy logic controlled PV system Figure 4.4 shows the block diagram of Fuzzy logic controlled PV system that uses SEPIC which is developed in this work. It consists of a solar panel, fuzzy logic controller, SEPIC converter, battery, inverter and load. This work is developed using Matlab simulink and its operation is verified. The fuzzy logic controller senses the I pv and V pv from the PV panel. A stand-alone PV system, which has a fuzzy logic based MPPT controller, SEPIC, battery and inverter, is developed in this work for AC loads. In this photovoltaic system, fuzzy logic based MPPT tracking control is given to maximize the PV output power during the sunshine hours. A battery that is charged during the daylight hours is utilized, in this case, to feed the loads during night hours or during the weak weather conditions. The controller is designed with the designed 750W solar PV panel to feed an average demand of 250W. The SEPIC converter provides a constant DC bus voltage and its duty cycle is controlled by the fuzzy logic MPPT controller. It is simulated using MATLAB

10 65 Simulink and the results are presented to demonstrate the performance of the MPPT controller for various load conditions. 4.4 DESIGN CONSIDERATION The solar - PV cell produces electricity directly by converting the solar energy into electrical energy. The output voltage and current of the single PV cell are quite small to be used practically and hence, the PV cells are used in series-parallel combinations, called PV array and is used to produce significant level of voltage and current. In this work, the solar PV energy generation system is designed to feed an average load of 250W.By considering this load, the rating is selected for solar PV panel and it is 750W. For utilization of energy produced by the 750W solar PV system, an energy storage system, which can store the surplus energy and delivering it to the load, is used. The standard panels are connected in parallel combination to realize 750W rated power system. Figure 4.5. shows the system composition with the MPPT controller. This system consists of solar PV panel, an isolated SEPIC converter, energy storage system, a VSI and a low pass filter to feed consumer loads. Figure 4.5 SEPIC with MPPT controller

11 66 The variable DC voltage of the PV panel is converted to a constant DC voltage using a SEPIC converter. The output of PV array voltage is 106V and SEPIC boosts that voltage to 230V to provide a constant DC link for the battery charging. The SEPIC Converter is controlled by a MPPT approach to ensure maximum energy capture MPPT Control Algorithm A PV array is a nonlinear power source, which under constant uniform irradiance has a current-voltage (I-V) characteristic as shown in Figure 4.2. There is a unique point of the curve, called the Maximum Power Point (MPP), at which the array operates with maximum efficiency and produces maximum output power. So it is necessary to constantly track the MPP of the solar array. There are various techniques used for tracking maximum power like Perturb and Observe (P&O), Incremental Conductance (IC) and Fuzzy Logic Control (FLC). Among these algorithms, FLC is equipped in this work. FLC has the advantage of an easy implementation. It does not need any accurate mathematical model of the system and it is very suitable for nonlinear system. It works under intelligent control method. In FLC, the given crisp set values are converted into linguistic variables by using fuzzification unit. The reference value is decided and according to the actual value of the voltage and power, the error value is calculated. By using this error value the change in error value is calculated. The two FLC input variables are the error E and change of error CE at sampled time k as shown in equations (4.2) and (4.3). E(k) CE(k) P(k) P(k 1) V(k) V(k 1) = (4.2) = E(k) E(k 1) (4.3) where P(k) is the output power of the PV generator at the k th sampling instant. The input E(k) indicates whether the operating point at the instant k is located

12 67 on the left or right of the maximum power point on the PV Characteristics. while the input CE (k) expresses the moving direction of this point. The fuzzy inference is carried out by using Mamdani s method and the defuzzification uses the centre of gravity to compute the output of the FLC which is the duty cycle d of the SEPIC. The control rules are indicated in Table 4.2 with E and CE as inputs and dα as the output. The rule evaluation uses the fuzzified output from the fuzzification stage and the rule base to produce the fuzzy output variable. The defuzzification stage converts the fuzzy output variable that is produced from the rule evaluation into a single real number. In the rule, NB stands for negative big, NS is negative small, ZE is zero, PS is positive small and PB is positive big. Table 4.2 Fuzzy Rule Table CE E NB NS ZE PS PB NB ZE NS NS NB NB NS PS ZE NS NS NB ZE PS PS ZE NS NS PS PB PS PS ZE NS PB PB PB PS PS ZE The membership functions for error (E) change of error (CE) and output for shown Figure 4.6. SEPIC FLC Error (5) (Mamdani) 25 rules output (5) change of Error (5) SEPIC FLC: 2 inputs, 1 Output, 25 rules Figure 4.6 Membership function for MPP

13 68 The details of the labels in the Figure 4.6 are as follows: Name = SEPIC_FLC Type = mamdani NumInputs = 2 InLabels = error, Change of error NumOutputs = 1 OutLabels = output1 NumRules = 25 AndMethod = min OrMethod = max ImpMethod = min AggMethod = max DefuzzMethod = centroid Figure 4.7 Membership function output variable Figure 4.8 Membership function input variable (error)

14 69 Figure 4.9 Membership function input variable (change of error) The Figures 4.7, 4.8 and 4.9 show the membership function for output variable, input error variable and input change of error respectively. Figure 4.10 shows the rule viewer and the rules are given as follows. There are 25 rules are framed to generate the output. Figure 4.10 shows the rule viewer. Figure 4.10 Rule viewer

15 70 50 ou tp ut change of error error Figure 4.11 surface viewer Figure 4.11 shows the surface viewer for the fuzzy logic controller. In the defuzzification, the output is converted from a linguistic variable to a numerical crisp one again by using membership functions. There are different methods to transform the linguistic variables into crisp values. The most popular method is the center of gravity method and it is used in this work SEPIC Converter The important requirement of any DC DC converter used in the MPPT scheme is that it should have a low input-current ripple. The Single Ended Primary Inductance Converter (SEPIC) topology with isolation is used in this work and the converter is operated in Continuous Conduction Mode (CCM) and in boost mode. Figure 4.12 shows the isolated topology of SEPIC converter. The inductor L 2 is used as an isolated component using coupled inductor.

16 71 Figure 4.12 Isolated SEPIC topology The converter has input inductance L 1 and input capacitance C 1 with the values of 1.27mH and 111.3µF respectively. The isolation inductor has the turns ratio of 1:5. The output capacitance has the value of 22.4µF. The duty cycle d can be determined from the steady state conduction using the equation (4.3) V V o g = N 2 d (4.3) N 1 d DESCRIPTION OF WORKING The variable DC voltage of the PV panel is converted to a constant DC voltage using a SEPIC converter. The maximum power output of PV panel is the product of V mp and I mp. According to this, the maximum power

17 72 output power of the PV panel is 750W. The irradiation level is set at 500W/m 2 at 0.1s and set at 900W/m 2 at 0.2s. The maximum power output depends on the solar irradiation level. Up to 0.1s, the solar voltage is around 110V and the current is near 3.5A. The maximum power at that condition is 380W. From 0.2s, the current level is increased to the value of 4A and the PV voltage is around 175V power output of PV panel is increased to 700W. To obtain the maximum power at any insolation, the MPPT algorithm is used. Here, FLC is proposed. The output of the fuzzy controller is given as control signal which is compared with carrier of 100 KHz to generate PWM output pulses and is given as duty cycle to the gate of the switch. The converter output voltage is around 250V. The output of the converter is given to the energy storage element. Here a lead acid battery is used as a energy storage element. The rating of the battery is 250V, 15AH. The stored energy is given as input to the single phase voltage source inverter (VSI). The output of the VSI is given to the filter circuit with values of filter capacitance as 13.3µF and filter inductance as 2.45mH. Finally, the filter output is given to a resistive load having resistance of 10 Ω Simulation Figure 4.13 shows the main Matlab Simulink circuit for PV system with SEPIC converter with the isolated inductor. Two panels are connected to supply the SEPIC converter. The subsystem of fuzzy logic controller is shown in Figure 4.14 which regulates the duty cycle of SEPIC.

18 73 Figure 4.13 Matlab Simulink for PV systems with SEPIC converter Figure 4.14 Subsystem for Fuzzy logic controller

19 74 From the Figure 4.15 shows the steady state output voltage of the PV panel when the irradiation 500w/m S o l a r O u t p u t v o l t a g e ( V ) S o l a r O u t p u t v o l t a g e ( V ) Time(s) Time(s) Figure 4.15 solar output voltage Figure 4.16 shows the simulated SEPIC simulated output voltage using fuzzy logic controller which is maintained at 230 V. The percentage peak to peak input output Voltage is also shown in Figure The steady state ripple is from to which is 1.22V (0.53% of output valtage) only which is very low and within the permissible limit and this has been shown to verify the controller performance. This proves that during the steady state, the output voltage has negligible ripple.

20 75 S E P I C v o l t a g e ( V ) Time(s) Time(s) Figure 4.16 SEPIC output voltage with steady state S E P I C v o l t a g e ( V ) S E P IC O u tp u t C u r r e n t(a ) Time(s) Figure 4.17 SEPIC output current

21 76 I n v e r t e r O u t p u t V o l t a g e ( V ) In v e r t e r o u t p u t c u r r e n t (A ) Time(s) Figure 4.18 Inverter output voltage Time(s) Figure 4.19 Inverter output current Figure 4.17 shows the output current of SEPIC converter. Figures 4.18 and 4.19 shows the inverter output voltage and current respectively.

22 CHAPTER SUMMARY A stand-alone solar-pv energy generation system with a SEPIC converter that uses fuzzy logic controller is designed and its performance is verified by doing simulation in Matlab simulink. The Fuzzy logic MPPT controller is designed and it has performed satisfactorily by generating required output voltage from SEPIC which is used to charge a battery. The battery is as an input source for a inverter which generates 230V output for ac loads.