A comprehensive simulator for assessing the reliability of a photovoltaic panel peak power tracking system

Abstract

When designing a maximum power point tracking (MPPT) algorithm, it is often difficult to correctly predict, before field testing, the behavior of this MPPT under varying solar irradiation on photovoltaic (PV) panels. A solution to this problem is to design a maximum power point trackers simulator of a PV system used to test MPPT algorithms. This simulator must have the same role as the MPPT card of the PV panel and thus will fully emulate the response of a real MPPT card of the PV panel. Therefore, it is a good substitute to help to test the peak power trackers of the PV system in the laboratory. This paper describes a simple peak power trackers simulator of the PV system which has a short response time thus, can be used to test MPPT algorithms under very rapid variation condition. The obtained results and the theoretical operation confirm the reliability and the superior performance of the proposed model.

Graphical abstract

Keywords

photovoltaic module / DC-DC converter / design / maximum power point tracking (MPPT) card / microprocessor

1 Introduction

Solar panels collect the energy of the sun and convert the solar energy into electrical energy. Unfortunately, the sun is not consistent throughout the day due to cloud cover and the angle of the sun relative to the position of the solar panel. In addition, the intensity of the sun varies according to season and geographical location. Furthermore the characteristic curve of a solar cell exhibits a nonlinear voltage-current relationship [1]. Therefore, a controller named the maximum power point tracker (MPPT) which is an essential part of a photovoltaic system, is adopted to automatically find the maximum power operating point at all environmental conditions and then force the PV system to operate at this point (MPP), to ensure the optimal use of the available solar energy [1,2]. This function is implemented by suitably controlling the power processing circuit that is almost always used as an interface between the PV generator and the load or the energy accumulator.

Peak power tracking algorithms provide the theoretical means to achieve the MPP of solar panels, and these algorithms can be realized in many different forms of hardware and software [3]. Field testing of real photovoltaic systems has drawbacks. For instance, it is costly, heavily dependent on the weather condition and time consuming. Therefore, the MPPT simulator of a PV system is a good substitute to help to test the peak power tracker algorithms of the PV system in the laboratory. Furthermore, the use of the simulator also makes it possible to impose any time variation of weather conditions in order to test the MPPT in critical situations, based on the user’s choice. In most MPPT photovoltaic experiments, there often arises the need to test various algorithms under controlled conditions of solar irradiation. In the last few years, several MPPT techniques, such as the perturb and observe (P&O) method, the incremental conductance (IC) method, the artificial neural network method, the fuzzy logic method [4,5] etc., have been developed and proposed to maintain the PV arrays operating at their MPPs, of which the P&O technique has been widely used because of its ease of implementation [6].

This paper describes a comprehensive ISIS Proteus simulator of MPPT card for photovoltaic system. The design of peak power tracking for a photovoltaic system using buck-boost DC-DC converter topology is proposed. A novel modeling technique is presented, its implementation in ISIS Proteus software is explained, and the modeling accuracy is examined. The electronic circuit corresponding to the buck-boost converter with the numerical control MPPT is described in detail. The theoretical operation and the results obtained are discussed. The contributions of this paper to the more precise and comprehensive study of a PV system peak power tracker are elaborated on and the conclusion is drawn.

2 Description of the PV system studied

The basic design for the photovoltaic system peak power tracker consists of the solar panel, DC-DC converters connected in a series, the microcontroller, voltage and current sensors and the load. The module current is monitored by a Hall Effect current sensor. As the panel output power varies, the sensor sends the current values to the microcontroller. The module output voltage is decreased by a factor using a voltage divider which allows for a voltage level which the microcontroller can handle [7]. The algorithm uses the P&O method, which will be described later. At this step, an algorithm adjusts the DC-DC conversion.

This model is known to have a better accuracy when the irradiance varies slowly that allows for a more accurate prediction of the PV system performance. The main concern is to make the design easier and straightforward on the whole system and evaluate the tracking accuracy, steady-state oscillations and efficiency.

3 Theoretical backgrounds

In this paper, the five parameter model (The single diode model for solar cells Fig. 1) is chosen because most of the time the results have a high degree of consistency with experimental data and they are relatively simple to implement and analyze [8].

The I-V characteristic equation is given by

(1)I=I_{\mathrm{ph}}-I_{\mathrm{0}}\left[\exp \left({\displaystyle \frac{V+I{R}_{\mathrm{s}}}{A{V}_{\mathrm{t}}}}\right)-\mathrm{1}\right]-{\displaystyle \frac{V+I{R}_{\mathrm{s}}}{R_{\mathrm{p}}}},

where I_{\mathrm{ph}} is the photo generated current (proportional to incident radiation), I₀ is the saturation current of diode, R_s is the cell series resistance, R_p is the cell parallel resistance, V_t is the thermal voltage, and A is the diode quality factor.

The PV model parameters I_ph, I₀, R_s and R_p are determined based on electrical parameters I_sc, I_mp, V_mp, V_oc, and A. The aim is to find the model parameters such that the resulted I-V curve accurately matches the experimental curve. These parameters are obtained by solving the fundamental Eq. (1) or the key points. The values for I_ph, I₀, R_s and R_p are then determined through an iterative procedure [9].

Figure 2 shows the experimental and simulated curves of the PV module ISOFOTON I-75 based on experimental input data (The main parameters of the PV module ISOFOTON I-75 is shown in Table 1). The I-V experimental characteristic is measured by an experimental bench composed of an I-V curve tracer, a personal computer (PC), a temperature probe placed on the surface of the panel and a pyranometer. In Fig. 2, the simulated results have been compared with the experimental data. It is observed that the simulated and experimental results match accurately at three key points: open circuit V_oc, maximum power P_m, and short circuit I_sc. The curves are also reasonably close at other points. Thus the results are validated with experimental study.

Figure 3 illustrates the I-V characteristics for different illuminations. Different illuminations can be represented by varying the current source I_ph. In this simulation, the current source I_ph is stepped through 3 different values using the TORCH (Fig. 4).

4 System design

The peak power tracker is a microprocessor controlled DC-DC converter used by a photovoltaic power system. The microprocessor tries to maximize the output power from the solar panel by controlling the conversion ratio of the DC-DC converter to keep the solar panel operating at its MPP.

A DC-DC converter acts as an interface between the load and the module. When a load is connected to the PV module, a load line is imposed on the I-V curve [10]. The voltage and current of the PV module are at the point where the load line intersects with the I-V curve [11]. Then, the power is simply the multiplication of the current and voltage at the intersection point. The load line position on the I-V curve depends on the impedance of the load. By varying the ratio of duty cycle, the impedance of load, as it appears by the source, is varied and matched at the peak power point with the source so as to convert the maximum power. Therefore, a DC-DC converter is needed between the PV module and load.

The buck-boost mode DC-DC converter is the last and most important type of switching regulator. In this converter, the buck and boost topologies are combined into one [12]. This converter provides an output voltage that may be less than or greater than the input voltage. The buck-boost converter steps the voltage down when the duty cycle is less than 50% and steps it up when the duty cycle is greater than 50% [13]. The circuit of the buck-boost converter is depicted in Fig. 5.

The microprocessor used to control the DC-DC converter is a Microchip PIC16F876A (see data sheet for more complete information). The micro-processor is clocked at 8 MHz by the crystal XTAL18. The PWM output of the PIC is used to control the duty cycle of the DC-DC converter which sets its voltage conversion ratio. The frequency of the PWM is set to 100 kHz by the PIC software. The microcontroller provides the control in the system in this paper. The choice of microcontroller for the system dictates much of the cost, performance, and flexibility of the entire system. The microcontroller 16F876A is demonstrated in Fig. 6.

The conversion ratio of the input voltage to output voltage of the DC-DC converter must also change to keep the solar panel voltage at the MPP. The peak power tracker uses an iterative approach to finding this constantly changing MPP. The PPT uses a microprocessor to measure the watts generated by the solar panel. It then controls the conversion ratio of the DC-DC converter to implement the P&O algorithm [14]. The PIC uses the LED- GREEN D4 as indicators to show the software state.

4.1 Perturb and observe method

The concept behind the “perturb and observe (P&O)” method is to modify the operating voltage or current of the photovoltaic panel until a maximum power is obtained from it. For example, if increasing the voltage to a panel increases the power output of the panel, the system continues increasing the operating voltage until the power output begins to decrease. Once this happens, the voltage is decreased to get back toward the MPP [2]. This perturbation continues indefinitely. Thus, the power output value oscillates around a MPP and never stabilizes [15]. The flowchart of the P&O algorithm is displayed in Fig. 7.

P&O is simple to implement and thus can be implemented quickly. The program in language C is implemented in the microprocessor PIC which provides the signal. The PIC calculates the solar watts generated by reading the voltage and current of the solar panels.

4.2 Voltage sensing

In order for the microprocessor to control the duty cycle of the converter, it needs to obtain voltage samples from the solar panel output. This will be done using a very simple method of voltage sensing. Usually, the microprocessor would be able to take the voltage directly from a source to sense the voltage. Nevertheless, the voltage coming from the solar panel will be much too high for the microprocessor to handle. The maximum amount of voltage that the microprocessor will take will be 5V. Any voltage higher than this amount would destroy the microprocessor, and the system would fail to monitor and maintain the peak power operating point all together. Knowing this, it is with great care that the voltage divider is installed in such a way that it will always output a voltage that is much lower than the threshold voltage of what the microprocessor can handle.

The installation of RD1 and RD2 makes a voltage divider (Fig. 8) which reduces the input voltage to the 5 V range that can be read by the PIC.

4.3 Current sensing

In order for the MPPT controller to measure the current provided by the solar panel, a current sensor (MAX4173TESA) (Fig. 9) is placed in series between the solar panel and the DC-DC converter [7,14].

5 Results and discussion

This section presents in details the implementation of P&O MPPT using buck-boost converter. Some results such as current, voltage and output power for different conditions have been recorded. The simulation has been accomplished in the ISIS Proteus software. Simulink implementation of the MPPT system simulator is displayed in Fig. 10.

The photovoltaic module ISOFOTON I-75 consists of 36 monocrystalline silicon cells connected in series, with a maximum power of 75.08 W, a current of 4.34 A and an optimal voltage of 17.3 V. The whole system was simulated for different solar energy conditions, at standard test conditions (STC) (Fig. 11), under three different solar irradiation levels (Fig. 12) and under rapidly varying solar irradiation (Fig. 13), to test the proposed system under specific atmospheric conditions.

5.1 At STC

The PWM output of the PIC is used to control the duty cycle of the DC-DC converter which sets its voltage for keeping the solar panel operating at its MPP. By changing the duty cycle of the converter, the intersection point moves to the knee of the IV curve results in MPP. Figure 11 shows the simulation results of the MPP searching by the convention algorithm. The P&O algorithm is able to reach the desired voltage at t = 0.4 s. With the fast tuning in the voltage of the PV module, the MPP can be reached rapidly. The theoretical operation gives a 75.63 W maximum power, a maximum voltage of 17.23 V and a maximum current of 4.39 A at STC.

This model is the topology that exhibits better performance than other topologies such as analog MPPT [11,16]. It is a more efficient, accurate, convenient and low cost technique without the need for complicated mathematical operations and is independent of device physics. The technique is based on the perturbation of voltage (or current) using the present P and previous P_old operating power, respectively. If P is improved, the direction of perturbation is retained; otherwise, the direction is reversed accordingly. So, this model can be easily implemented using low cost microcontrollers and writing the program needs just a little literacy in C language and familiarity with programming [11].

Oscillations around the MPP in steady-state operating appear, as shown in Fig. 12. Thus, the system has power losses which is equal to 0.55 W (the error in P_m is equal to 0.73%), therefore, is reasonable and acceptable error. For P&O, steady-state oscillation occurs after the MPP is reached because the perturbation continuously changes in both directions to maintain the MPP [10,11]. If the tracking step is continuously get smaller, oscillations could be minimized and consequently the power loss. Nevertheless, the response of the algorithm becomes slower [12].

A comparative study in terms of MPPT performance is also performed by Hussein et al. [16] between IncCond and P&O control. During this test, the efficiency displayed by IncCond command is 89.9% compared to 81.5% of the P&O control. This difference is mainly caused by the relatively small changes around the PPM engendered by IncCond command. Besides, various tests extracted from the literature show that P&O control can be effective in certain conditions [17]. Therefore, it is not wise to suggest such method of research is better than another because the test conditions and increment variable values are not similar.

5.2 Under different solar irradiation levels

The simulation is conducted for PV system under three different solar irradiation levels; the solar irradiation is changed from high to low (See Fig. 14).

The simulation showed that the proposed MPPT system has reached the MPPs. The model performs accurately and faster during the slowly decrement of solar irradiation intensity. Indeed, it is known that this type of command obtains a higher efficiency over a relatively sunny day, where the MPP evolves slowly and proportionately to the sun.

The MPP changes depending on the solar irradiation level because the P&O algorithm not only determines the new duty cycle where the system should move next but also replaces the old values with the new ones. Once the peak power is reached, the algorithm will stay and oscillate around the MPP. The microcontroller was responsible for regulating the system and assuring the voltage applied to the load would deliver the maximum power.

The perturb oscillation around the peak power point of the P&O method to track the peak power under varying atmospheric condition is overcome by the IC method. The IC algorithm has advantages over the P&O algorithm in that it can determine the time when the MPPT has reached the MPP, and the place where the P&O oscillates around the MPP [10]. In addition, the incremental conductance can rapidly track the increase and decrease of the solar irradiation intensity with higher precision than the P&O. The drawback of this algorithm is the increased complexity [4,10].

5.3 Under rapid-varying solar irradiation

The simulation is conducted for the whole system under fast-changing solar irradiation levels; the solar irradiation is changed from high to low.

The simulation showed that the MPPT failed to track the MPP. In the case of rapid-varying solar irradiation, the P&O method loses the direction of the new MPP and the tracking is driven into a wrong direction. This is one of the major problems of the P&O method [15–18]. From the previous section, it can be concluded that failure of the P&O algorithm to follow the rapid-varying atmospheric conditions is caused by its inability to relate the change in the PV system power to the change in the atmospheric conditions.

The P&O method often fails to track the true MPP under special conditions such as partial shading and modules irregularities, because the PV curves are characterized by multiple peaks (several local and one global) [18,19]. Since the P&O algorithm could not distinguish the correct peak, its usefulness under such conditions diminishes rapidly. Therefore, for more reliability under special condition, a novel algorithm is required to modulate the duty cycle of the DC-DC converter in order to ensure the fast MPPT process.

6 Conclusions

A simulator of PV system peak power tracker was developed to test MPPT algorithms under varying solar irradiations and to realize a simple digital controller capable of optimizing the amount of power recovered from a solar panel over a range of environmental conditions. Besides, a simple model for a photovoltaic panel was realized. Moreover, a prototype PPT system was developed using the ISIS Proteus software, which was able to analyze and to study the performances of MPPT techniques. The basic design of the peak power tracker is to read the voltage and current levels at the solar panel simulator output, process these values using the P&O algorithm, and adjust the voltage in order to obtain the maximum power. This program was implemented using a 16F876A microcontroller. The obtained results confirmed the superior performance of the proposed model. It is envisaged that the proposed work can be very helpful for professionals who require accurate photovoltaic simulator to study their systems and with the properly functioning software.

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