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This paper explores three maximum power point tracking (MPPT) techniques, i.e. the Incremental Conductance (IC), Perturb and Observe (P&O) and Variable Step Size Incremental Resistance (VSSIR) techniques. The MPPT techniques are firstly simulated on Matlab/Simulink platform. In order to realise the MPPT techniques on simulation, a DC-DC boost conve...
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... is implemented, the MPPT technique will regulate the amount of power drawn from the PV cell by regulating the duty cycle of the DC-DC converter. The MPPT technique will also ensure that the PV cell does not operate close to open circuit voltage or short circuit current. The implementation of an MPPT technique on a standalone PV system is shown in Fig. 1. This paper will investigates the three MPPT techniques, out of which Incremental Conductance (IC) and Perturb and Observe (P&O) which are simple and commonly used while Variable Step Size Incremental Resistance (VSSIR) is a recently proposed (2011) complex MPPT technique [3]. Modelling of a PV panel and DC-DC converter is detailed and ...
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... model shown in Fig.10 is an alternative model to the Simulink model in Fig.7. The Matlab script takes solar insolation and cell temperature as inputs and outputs the P-V and I-V output characteristic of a particular PV panel. ...
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... functions on the script are derived from equation (4), (5), (6) and (7). Fig.11 shows the P-V output characteristic of the solar shell SQ80. ...
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... with a switching period Ts and a duty cycle D, The relationship between the input voltage V i and output voltage V o is shown by (8) when the switch T is on and (9) when the switch T is off. Matlab/Simulink can accommodate various design topologies of the DC-DC boost converter. The converter can be modelled using physical components as shown in Fig. 12 or using simulation blocks as shown in Fig. 13 (Variable topology model). The simulations blocks can be used to model complex systems and electric circuit diagrams. The DC-DC converter can be modelled by simulation blocks due to the dynamic signal flow interface available on Matlab Simulink [6]. The results obtained from the simulation ...
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... The relationship between the input voltage V i and output voltage V o is shown by (8) when the switch T is on and (9) when the switch T is off. Matlab/Simulink can accommodate various design topologies of the DC-DC boost converter. The converter can be modelled using physical components as shown in Fig. 12 or using simulation blocks as shown in Fig. 13 (Variable topology model). The simulations blocks can be used to model complex systems and electric circuit diagrams. The DC-DC converter can be modelled by simulation blocks due to the dynamic signal flow interface available on Matlab Simulink [6]. The results obtained from the simulation blocks can be studied and compared with ...
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... input to the converter in Fig. 13 is a PWM signal, PV voltage and current. The output is a regulated voltage level. When the switch T is on, the output of the AND gate is a logic 1 which enables multiplication blocks of the system. During this phase the open loop operation of the converter is governed by (10) and (11). When the switch T is off, the output of the AND ...
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... of implementing a complete solar system is high. The performance of the solar system and MPPT techniques can be studied and related through Matlab software simulation. The MPPT techniques can be fine-tuned for high efficiency performance based on the results and analysis of the simulations. The PV panel model in Fig. 7 and the Boost converter in Fig. 12 were used to conduct the simulations. The simulation result in Fig. 14 shows the effect of change in solar insolation on the PV system with a constant cell temperature. The VSSIR has Iref max =0.3 and Iref=0.025. The perturbation and incremental value of the IC technique is 10%. The simulation initially has solar insolation of 1000 ...
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... solar system and MPPT techniques can be studied and related through Matlab software simulation. The MPPT techniques can be fine-tuned for high efficiency performance based on the results and analysis of the simulations. The PV panel model in Fig. 7 and the Boost converter in Fig. 12 were used to conduct the simulations. The simulation result in Fig. 14 shows the effect of change in solar insolation on the PV system with a constant cell temperature. The VSSIR has Iref max =0.3 and Iref=0.025. The perturbation and incremental value of the IC technique is 10%. The simulation initially has solar insolation of 1000 W/m2 at 0s. The solar insolation is decreased to 250 W/m2 at 2s and then ...
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... of 1000 W/m2 at 0s. The solar insolation is decreased to 250 W/m2 at 2s and then increased to 650 W/m2 at 4s. The insolation is decreased to 10 W/m2 at 6s. At 7.5s, a positive step of 50 W/m2 is applied. At 9s a negative step of 10 W/m2 is applied. The cell temperature is kept at a constant of 25°C throughout the simulation. The results in Fig. 14 show that the P&O (green) will oscillate around the MPP region as expected. Despite the oscillation, the P&O technique delivers the expected power for different values of solar insolation. The results also show that the IC technique converges faster than the P&O and does not oscilate for low and high values of solar insolation. ...
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... simulation in Fig. 15 shows the effect of a change in cell temperature on a PV system with a constant solar insolation. The VSSIR has Iref max =0.3 and Iref=0.025. The perturbation and incremental value of the IC technique is 0.1. The simulations were conducted at a constant cell insolation of 1000 W/m 2 . The simulation starts at a cell temperature (T) ...
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... to 17°C, at 7s. T is increased to 25°C, at 8s and maintained for the remainder of the simulation. At low temperatures the P&O technique oscillates around the MPP. Despite this oscillation, it can be seen from the simulation that the power delivered by P&O is equivalent to the power delivered by IC and VSSIR. The point of interest on the in Fig. 15 is at 3.3s and 7s when a negative step change of 5°C and a positive step change of 2°C is introduced respectively. The VSSIR, P&O and IC technique are able to sense the change in cell temperature and adjust the duty cycle. However at 7s the VSSIR and IC techniques are not able to sense the change in cell temperature. The P&O technique ...
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... Leonardo microprocessor limited to a duty cycle of between 0.2 and 0.8. The apparatus used for the experiment are a 5V and ±15V Power supply, digital multimeter and scope, shell solar SQ80 PV panel, 900uf capacitors, 0.3mH inductor, 39 lamp loads, a voltage and current sensing module, MOSFET and a diode. The experimental setup is shown in Fig. 16. The experiments were conducted at the University of Cape Town, Engineering Power Laboratory from the 22 September to 08 October 2013. Initially the boost converter's standalone performance was tested. The input to the converter was a 10V regulated supply. Fig. 17 shows the output response of the converter switching at 32 kHz with a ...
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... and current sensing module, MOSFET and a diode. The experimental setup is shown in Fig. 16. The experiments were conducted at the University of Cape Town, Engineering Power Laboratory from the 22 September to 08 October 2013. Initially the boost converter's standalone performance was tested. The input to the converter was a 10V regulated supply. Fig. 17 shows the output response of the converter switching at 32 kHz with a duty cycle of 50%. The second part of the experiment was to test the PV panel system using the experimental setup shown in Fig. 16. A code was run on the Arduino to increment the duty cycle from 0.1 to 0.9 by increments of 0.02. A total of 40 samples were taken in 2 ...
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... to 08 October 2013. Initially the boost converter's standalone performance was tested. The input to the converter was a 10V regulated supply. Fig. 17 shows the output response of the converter switching at 32 kHz with a duty cycle of 50%. The second part of the experiment was to test the PV panel system using the experimental setup shown in Fig. 16. A code was run on the Arduino to increment the duty cycle from 0.1 to 0.9 by increments of 0.02. A total of 40 samples were taken in 2 seconds. At every change in duty cycle, the Arduino saves the corresponding voltage and current measurements and saves them on a downloadable file. For sufficient data collection, the experiment was ...
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... taken in 2 seconds. At every change in duty cycle, the Arduino saves the corresponding voltage and current measurements and saves them on a downloadable file. For sufficient data collection, the experiment was run on three different atmospheric conditions. The data is used to plot the I-V output characteristic of the PV panel which is given in Fig. 18. This is compared to the I-V output characteristic obtained from the simulations which is given in Fig. 19. The graphs in Fig. 18 and Fig. 19 show a close correlation, with an error of 11.01%, between the actual PV panel and the PV model on Simulink. The simulations on Matlab/Simulink are run with ideal component values and an ...
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... measurements and saves them on a downloadable file. For sufficient data collection, the experiment was run on three different atmospheric conditions. The data is used to plot the I-V output characteristic of the PV panel which is given in Fig. 18. This is compared to the I-V output characteristic obtained from the simulations which is given in Fig. 19. The graphs in Fig. 18 and Fig. 19 show a close correlation, with an error of 11.01%, between the actual PV panel and the PV model on Simulink. The simulations on Matlab/Simulink are run with ideal component values and an assumption that the solar insolation and cell Temperature is constant. The hardware experiment is made of non-ideal ...
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... them on a downloadable file. For sufficient data collection, the experiment was run on three different atmospheric conditions. The data is used to plot the I-V output characteristic of the PV panel which is given in Fig. 18. This is compared to the I-V output characteristic obtained from the simulations which is given in Fig. 19. The graphs in Fig. 18 and Fig. 19 show a close correlation, with an error of 11.01%, between the actual PV panel and the PV model on Simulink. The simulations on Matlab/Simulink are run with ideal component values and an assumption that the solar insolation and cell Temperature is constant. The hardware experiment is made of non-ideal components with ...
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... downloadable file. For sufficient data collection, the experiment was run on three different atmospheric conditions. The data is used to plot the I-V output characteristic of the PV panel which is given in Fig. 18. This is compared to the I-V output characteristic obtained from the simulations which is given in Fig. 19. The graphs in Fig. 18 and Fig. 19 show a close correlation, with an error of 11.01%, between the actual PV panel and the PV model on Simulink. The simulations on Matlab/Simulink are run with ideal component values and an assumption that the solar insolation and cell Temperature is constant. The hardware experiment is made of non-ideal components with associated loses. ...
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... and current from a maximum voltage of V pv and current I pv to 5V and 0.5 A, respectively, to make it suitable for the Arduino reading. There is a percentage of accuracy lost during the scaling down of the sensors too. However, the results show that the simulated model can be used as a legible PV panel prototype. Based on the results given in Fig. 18 and Fig. 19, the MPPT techniques can be implemented on hardware and compared to ...
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... final part of the experiment tested the accuracy, error and simulation of the IC technique. The experimental setup in Fig. 16 was maintained. The output of the LEM current measurement module and the output of the voltage measurement module were connected to pins A0 and A5 of the Arduino Leonardo board respectively. The IC technique code programmed on the chip was used to track the MPP. Readings of the PV panel's input voltage and current, boost converter's ...
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... voltage from the experiment was monitored by a digital multimeter. Fig. 20 shows that there is an error between the simulated and experimental input voltage (PV panel voltage). The MPP of the hardware system can be compared to the MPP of the simulation system as shown in Fig. 20. The error associated with the simulation and experimental model in Fig.18 and Fig. 19 is the cause of error in results of A new method, the variable topology model, of modelling a boost converter was investigated. The error between the classic boost converter model and the variable topology model was 12.5%. The PV panel system was also successfully simulated on Matlab/Simulink using the classic boost circuit model and ...
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... The VSSIR technique adjusts the perturbation value automatically depending on how far it is operating from the MPP and how much variation exists. A feature that makes the VSSIR unique is the derivative ( / or / ) as it can be used to determine and control the variable increment value according to equation (14) [53]. ...
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