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“Minimization of Drift Through MPPT Algorithm Using SEPIC”
1Sweety Upadhyay, 2Mahendra Kumar Pradhan
1,2Dept. of Electronics & Communication Engineering, School of Engineering & IT, MATS University Arang (C.G)
Abstract — In this paper, we propose a solar/PV panel from which „dc‟ is supplied to the SEPIC (Single Ended Primary Inductance Converter) in which buck-boost operations are being performed and again „dc‟ is being produced as the output which is provided to the resistive load. Our main aim is to perform buck-boost operations using SEPIC. Frequently buck-boost converters are used but they provide inverted outputs, due to which SEPIC is being implemented.
Here we are using MPPT (Maximum Power Point Tracking) Algorithm to track the maximum power producer input device. However, P&O tracking method suffers from drift in case of an increase in insolation (G) and this drift effect is severe in case of a rapid increase in insolation. A modified P&O technique is used to avoid the drift problem. SEPIC converter is considered to validate the proposed drift free P&O MPPT. We are here using a PI/PID controller to minimize the error. Apart from the above, we can implement the whole strategy by using closed loop system through which output voltage can be set.
Keywords — Photovoltaic (PV), maximum power point tracking (MPPT): perturb and observe (P&O), single ended primary inductance converter (SEPIC), PI/PID controller.
I. INTRODUCTION
In recent decades, the continuous growth of energy demand from all around the world has urged the society to seek for alternative energies due to the depletion of conventional energy resources. Among the available alternative energies, photovoltaic (PV) energy is one of the most promising renewable energy; PV energy is clean, inexhaustible and free to harvest. The electrical energy production by using solar photovoltaic (PV) array has been drawing immense interest since solar energy is an environment friendly, maintenance-free and abundant source of energy. However, some drawbacks are associated with PV systems: high installation costs and low conversion efficiency. The commercial viability of PV power generation greatly depends on further improvement of conversion efficiency and reduction of cost. The power generated by a PV array largely depends on solar irradiance and temperature, different atmospheric conditions such as clouding and local surface reflectivity. The non-linear characteristics of PV array and the intermittent nature of sunlight hamper the proper utilization of PV array. For certain irradiance,
there is a unique maxima at a particular operating voltage in the power versus voltage (P-V) curve of PV array which is known as maximum power point (MPP).
The MPP keeps changing with solar irradiance and ambient temperature. To extract the maximum power at any environmental condition, maximum power point trackers (MPPTs) are usually employed. An MPPT is basically a dc–dc converter whose duty cycle is adjusted so that PV array is operated at the voltage corresponding to the MPP. The operating voltage and current are sensed and fed to the control unit for computation of duty cycle by any suitable MPPT algorithms that will ultimately lead the system operate at MPP. Until now a large number of MPPT techniques have been developed to increase the efficiency of the PV system. MPPT algorithms such as fractional open circuit voltage , fractional short circuit current , Hill-climbing , perturb and observe (P&O) , incremental conductance (IncCond) , incremental resistance (INR) , ripple correlation control (RCC) , fuzzy logic , neural network , particle swarm optimization (PSO) , sliding mode techniques are some of the MPPT techniques available in the literature.
Figure 1. Block diagram of PV system with MPPT control
In this paper we are using perturb and observe (P&O) algorithm because of its advantages like - it is PV array independent, a true MPPT can be implemented in both analog as well as digital platforms, no requirement of periodic tuning, easy to implement and requires fewer parameters.
PI/PID controllers are used to calculate an error value as the difference between a measured process variable and a desired set point and attempts to minimize the error over time by adjustment of a control variable.
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II. SEPIC
The single-ended primary-inductor converter (SEPIC) is a type of DC/DC converter allowing the voltage at its output to be greater than, less than, or equal to that at its input. The SEPIC Converter combines the best qualities of both the boost converter and flyback converter. A SEPIC is essentially a boost converter followed by a buck-boost converter, therefore it is similar to a traditional buck-boost converter, but has advantages of having non-inverted output (the output has the same voltage polarity as the input). This converter will reduce current ripples at the input for low level DC outputs, thus eliminating the need for a high frequency filter at the AC side, and the voltage stresses on the switches are reduced. While this converter is very good for high voltage applications when operated in CCM, it is better for low voltage options only when operated in DCM mode.
Figure 2. Schematic of SEPIC
The schematic diagram for a basic SEPIC is shown in Figure 2. SEPIC exchanges energy between the capacitors and inductors in order to convert from one voltage to another. The amount of energy exchanged is controlled by switch S1, which is typically a transistor such as a MOSFET. MOSFETs offer much higher input impedance and lower voltage drop and do not require biasing resistors as MOSFET switching is controlled by differences in voltage.
During a SEPIC's steady-state operation, the average voltage across capacitor C1 (VC1) is equal to the input voltage (Vin). Because capacitor C1 blocks direct current (DC), the average current through it (IC1) is zero, making inductor L2 the only source of DC load current.
Therefore, the average current through inductor L2 (IL2) is the same as the average load current. When switch S1 is turned on, current IL1 increases and the current IL2 goes more negative, voltage VC1 is approximately VIN, the voltage VL2 is approximately −VIN. Therefore, the capacitor C1 supplies the energy to increase the magnitude of the current in IL2 and thus increase the energy stored in L2. When switch S1 is turned off, the current IC1 becomes the same as the current IL1, since inductors do not allow instantaneous changes in current.
The current IL2 will continue in the negative direction, in fact it never reverses direction. It can be seen from the diagram that a negative IL2 will add to the current IL1 to increase the current delivered to the load. Thus it can be concluded, that while S1 is off, power is delivered to the load from both L2 and L1. C1, however is being charged by L1 during off cycle, and will in turn recharge L2 during the on cycle.
III. MPPT ALGORITHMS
There are many MPPT algorithms have been developed and implemented. In general, MPPT techniques can be divided into two categories, namely direct and indirect methods. Direct method of MPPT algorithms is independent from prior knowledge of PV module characteristics. The MPPT algorithms of this category are Perturb and Observe method (P&O), incremental conductance method (INCond.), feedback voltage or current, fuzzy logic method and neural network method.
Indirect method requires prior evaluation of PV generator. Methods like look-up table, open-circuit PV voltage, short circuit PV current etc. are included in indirect method.
Figure 3. Block diagram of the MPPT System A. Perturb and Observe (P&O) Method
The P&O method is most widely used in MPPT because of its simple structure and it requires only few parameters. Fig. 3.1 shows the flow chart of P&O method. It perturbs the PV array’s terminal voltage periodically, and then it compares the PV output power with that of the previous cycle of perturbation. When PV power and PV voltage increase at the same time and vice versa, a perturbation step size, ΔD will be added to the duty cycle, D to generate the next cycle of perturbation in order to force the operating point moving towards the MPP. When PV power increases and PV voltage decreases and vice versa, the perturbation step will be subtracted for the next cycle of perturbation.
This process will be carried on continuously until MPP is reached. However, the system will oscillate around the MPP throughout this process, and this will result in loss of energy. These oscillations can be minimized by reducing the perturbation step size but it slows down the MPP tracking system.
Figure 3.1 Flow chart of Perturb and Observe method (P&O)
B. Incremental Conductance Method (INCond.) In the incremental conductance method the MPP is tracked by comparing the instantaneous conductance (I/V) to the incremental conductance (ΔI/ΔV). Based on the flow chart in Fig. 3.2, V(k) and I(k) are the PV array output voltage and current at time k. The duty cycle, D of boost converter at which PV array is forced to operate at MPP. When the PV output voltage is constant but the output current increases, the duty cycle will increase. If the current decreases with the constant voltage, the duty cycle decreases. The work of the algorithm is to search a suitable duty cycle at which the incremental conductance equals to instantaneous conductance so that the PV systems always operate at the MPP.
Figure 3.2 Flow chart of Incremental Conduction method
The main advantage of INCond method is that it can produce good results under rapidly changing environment. INCond. Algorithm is able to achieve lower oscillation around MPP than P&O method.
However, it requires two sensors to measure the instantaneous PV output voltage and current, which results in high cost and complex circuit of the system.
C. Open-Circuit Voltage Method
This is a method based on the linear relationship between output voltage of the PV array at the MPP, VMPP and the PV array’s open circuit voltage, VOC in under varying temperature and solar irradiance. The PV array is temporarily isolated from MPPT, and then the open circuit voltage, VOC is measured periodically by shutting down the power converter momentarily. The MPPT calculates VMPP from the pre-set value of k1 and the calculated value of VOC. Then, the array’s voltage is varied until VMPP is reached. The shutdown of power converter periodically will incur temporary loss of power which results in power extracted will not be the maxima and PV array will never reach the MPP.
Figure 3.3 Flow chart of Constant Voltage method C. Short-Circuit Current Method
This method is quite similar to the open circuit voltage method. It is based on the linear relationship between PV array output current at MPP, IMPP and PV array short-circuit current, ISC. IMPP ≈ k2 ISC where k2 is the proportionality constant. The constant value of k2 also depends on characteristics of PV array. The procedure of short-circuit current method is the same as that of open- circuit voltage method, the flow chart of this method can be referred to the same flow chart as shown in Fig. 3.3.
An additional switch is added to the power converter and it is switched on momentarily to measure the short- circuit current, ISC by using a current sensor, then the MPP current is calculated. The output current of PV array is adjusted until the MPP current is reached. This process is repeated periodically. Like open-circuit voltage method, the MPP is never reached hence the output power produced will not be the maximum.
IV. PI/PID CONTOLLER
In this type of controllers the actuating signal consists of proportional error signal added with derivative and integral of the error signal. PID control combines the advantages of proportional, derivative and integral control actions. It is a control loop feedback mechanism continuously calculates an error value as the difference between a measured process variable and a desired setpoint and attempts to minimize the error. If the PID controller parameters (the gains of the proportional, integral and derivative terms) are chosen incorrectly, the controlled process input can be unstable, i.e., its output diverges, with or without oscillation, and is limited only by saturation or mechanical breakage.
A PI Controller (proportional-integral controller) is a special case of the PID controller in which the derivative (D) of the error is not used. The lack of derivative action may make the system steadier in the steady state in the case of noisy data. This is because derivative action is more sensitive to higher-frequency terms in the inputs.
Figure 4. Block diagram of PI Controller
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V. OPERATION
Figure 5.1MATLAB/Simulink model of conventional P&O method
Figure 5.2 MATLAB/simulink model of modified P&O method
Figure 5.3 MATLAB/Simulink model of comparison of conventional and modified P&O method Figure 5.1 shows the simulink model for minimization of drift using conventional P&O MPPT technique and Figure 5.2 shows the model for minimization of drift using modified P&O MPPT technique. The output voltage form the voltage measurement block is feedback and compared with a reference step signal. Now the signal is send to the PI/PID controller where the error value is calculated as the difference between the two signals. Suppose there is an overshoot then to suppress it and bring down to the desired level a negative gain is generated to saturate the signal and if there is an undershoot then a positive gain is generated in the gain block. The saturation of the signal i.e. limiting the signal to the upper and lower saturation values is done in the saturation block.
After saturating the signal it is send to the relational operator along with a carrier signal. Then the signal form the relational operator is multiplied with the output signal of the P&O and send to the switch i.e. the MOSFET and again in the circuit. This cycle continues until the MAXIMUM POWER POINT is reached. After reaching the MAXIMUM POWER POINT the cycle stops and we get the desired output signal.
The output1 of conventional P&O is shown in figure 6.1 and the output2 of modified P&O is shown in figure 6.2.
These two output signals are compared in a comparator which is shown in figure 6.3 where we can say that the output2 signal is drift free as compared to the output1 signal.
VI. EXPERIMENTAL RESULTS
Figure 6.1 Output of conventional P&O method
Figure 6.2 Output of modified P&O method
Figure 6.3 Output of comparison of conventional and modified P&O method
VII. CONCLUSION AND FUTURE SCOPE
Thus we can see from the experimental results that by applying P&O methods along with PI/PID controller drift can be minimized up to a large extent. We have compared the output of conventional P&O and modified P&O method in a comparator to clearly see the drift
minimization. We are here using PI/PID controller to minimize the error. Further the whole system is designed in a closed loop with the help of which the output voltage can be set.
In the output signal along with the drift minimization undershoot and overshoot can be seen. For desired and accurate output voltage signal and to reduce the undershoot and overshoot we can use Neural network in the place of PI Controller.
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