Implementation of a cost-effective fuzzy MPPT controller on the Arduino board

A 65 W polycrystalline PV module manufactured by the company Yingli Solar, Baoding, China, to evaluate the performance of the MPPT controller in a PV system was used. Table 1 shows the electrical parameters of the PV module (Robles Algarín et al., 2018).

To measure the current in the PV module and in the load, two ACS712 sensors were used, which provide an economical and accurate solution to measure current in AC or DC in industrial systems. It is a resistive sensor, based on the Hall effect, which delivers a voltage signal proportional to the measured current. Due to the characteristics of the PV system that was implemented in this research (65 W PV module and 12 V battery), the 20 A sensor (ACS712ELCTR-20A-T) with a variable output between 0 and 5 V with a sensitivity of 100 mV was used. Figure 2(a) shows the current sensor used.

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Figure 2:

To measure the voltage of the PV module and the battery, the voltage regulator FZ0430 was used, which is a voltage divider formed by two resistors of 75 and 30 kΩ that allow it to reduce the voltage measured in a factor of 0.2. In this way, a maximum voltage of 25 V can be measured for a 5 V processor. On an Arduino powered with 5 V that incorporates a 10-bit ADC, as is the case of this work, the resolution is 4.88 mV; but using the voltage regulator the resolution is 24.21 mV. In Figure 2(b) the regulator FZ0430 can be observed.

For the dc–dc converter, the Buck topology was chosen in order to guarantee a lower output voltage than the input voltage, which is suitable for the PV system used in this work: PV module of 65 W, 17.5 V, 3.71 A and load of 12 V. The converter was designed to operate in continuous conduction mode, ensuring that the current in the inductor does not reach zero during the switching cycle. Figure 3 shows the diagram of the dc–dc converter used.

For the design of the inductor, equation (1) was used, with output voltage V_o = 12 V, input voltage V_in = 16.08 V, critical output current i_o(crit) = 0.7308 A, ripple factor of 30% and sampling frequency f_s = 20KHz. The values of critical output current and input voltage correspond to the values obtained from the PV module operating at a solar irradiance of 200 W/m² and a temperature of 25°C (Robles Algarín et al., 2017). (1) Lmin≥Vo×(1−VoVin)2×i0(crit)×(0.3)×fs≥347.19µH.

To implement the inductor, a ferrite toroid was used, which was extracted from a recycled circuit board. The toroid has an external diameter of D_e=25 mm, internal diameter D_i=15 mm, height H=14 mm and relative permeability U_r=4480. A 20 awg wire was used, which supports a current of 11 A and a frequency of 27 kHz. The number of turns N of the inductor was calculated according to equation (2) (Tacca, 2009), for an inductor L=416.6 µH that was selected at 20% higher than the minimum value obtained in equation (1): (2)N=L0.0002×Ur×H×Ln(DeDi)=8.15.

With L = 416.6 µH, a new value of i_o(crit) = 0.6099 A is obtained. In this way, the ripple in the inductor current is shown in equation (3) (Robles Algarín et al., 2017): (3) ΔiL≤2×0.3×io(crit)=365.94mA

Using equation (4), the minimum value of the capacitor is obtained; for Δi_L = 365.94 mA, f_s = 20 kHz, ΔV = 5.5 V and ripple of 0.1%: (4) C≥ΔiL8×(0.001)×ΔV×fs≥415.8μF.

In the implementation of the converter, two 1,000 µF capacitors connected in series to obtain a value of C = 500 µF were used, according to what is established in equation (4).

To implement the diode of the converter, the schottky barrier diode SP10100C was used, which has working peak reverse voltage of 100 V and a maximum average forward rectified current of 10 A. This diode was extracted from a switched source of a led screen. In relation to the MOSFET, the IRF540N n-channel transistor was used, which is characterized by having an ultra-low on-resistance of 44 mΩ, drain-to-source breakdown voltage of 100 V, continuous drain current of 33 A, and fast switching of 11 ns.

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The main function of this block is to safely drive the turning on and off of the Mosfet IRF540N. For this work the driver IR2117 was selected, which has a voltage of 600 V, output current of 200 to 420 mA and output voltage of 10 to 20 V. In Figure 4 the diagram implemented with IR2117 is shown.

In order to properly drive the MOSFET with the IR2117, it was necessary to calculate the bootstrap circuit, which works as an overvoltage protection circuit. This circuit is made up of the capacitors C_b and the diode D₁. Equation (5) provided by the manufacturer to calculate the capacitor was used (HV Floating MOS-Gate Driver ICs, 2007). (5) Cb≥2×[2×Qg+Iqbsf+Qls+ICbsf]Vcc−Vf−VLS−VMin≥15.14nF, where Q_g is the gate charge of high-side IRF540N = 71 nC, Q_ls is the level shift charge required per cycle = 5 nC, f is the frequency of operation = 20 kHz, I_Cbs is the bootstrap capacitor leakage current = 0, for a tantalum capacitor, I_qbs is the maximum V_BS quiescent current = 240 µA, V_CC is the logic section voltage source = 22 V, V_f is the forward voltage drop across the bootstrap diode = 1 V, V_LS is the voltage drop across the low-side FET = 1.125 V, and V_Min is the minimum voltage between V_B and V_S = 0 V.

The value of the capacitor obtained in equation (5) is the minimum required for the correct operation of the bootstrap circuit. A lower capacitor value can cause overcharging; therefore, it is recommended to multiply the value of equation (5) by at least a factor of 15. In this sense, an electrolytic capacitor of commercial value of 22 uF/25 V was selected. Additionally, a 100 nF capacitor was connected in parallel with the 22 µF capacitor in order to reduce losses due to leakage currents. In relation to the diode needed for the bootstrap circuit, the MBR10150CT was used; which is ideal for this type of applications because it has a fast lock for reverse voltage when the MOSFET is in the On state.

To complete the design of the driver circuit, it was necessary to use an external voltage source to guarantee a stable voltage due to variations in the voltage of the PV module. For this reason, the XL6009 regulator was used, which was energized with the 65 W PV module. The XL6009 regulator is shown in Figure 5.

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Figure 5:

Considering that the Arduino board can only deliver maximum 5 V in the PWM output and that the IR2117 driver was configured with the logic of 0 to 22 V for the activation of the MOSFET transistor; a circuit that is responsible for the coupling of voltages and impedances between the Arduino and the IR2117 driver was designed. The coupling circuit is composed of the low power operational amplifier LM358 and the differential comparator LM339. The LM358 was configured as a voltage follower, while the LM339 was configured as a voltage comparator with a reference voltage of 2.5 V. In Figure 6 the coupling circuit is shown.

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The fuzzy controller was implemented on the Arduino Mega board (see Table 2), and its function is to adjust the duty cycle of the dc–dc converter depending on the variations in the operating temperature and solar irradiance; in order to track the MPP of the PV module.

Figure 7 shows the flowchart that represents the fuzzy algorithm that was implemented on the Arduino Mega board. A Mamdani fuzzy logic controller with the centroid defuzzification method was implemented.

The fuzzy controller has two inputs that correspond to the error E(k) and the change of error CE(k), and an output that corresponds to the increment in the duty cycle of the dc–dc converter. E(k) is the slope of the P–V curve of the PV module and allows us to establish the location of the MPP; while CE(k) allows knowing the direction of movement of the MPP. Equations (6) and (7) show the definition used for the inputs E(k) and CE(k) with sampling times k: (6) E(k)=P(k)−P(k−1)V(k)−V(k−1)=ΔPΔV, (7) CE(k)=E(k)−E(k−1)=ΔE.

The output ΔD(k) is responsible for modifying the duty cycle of the dc–dc converter in order to track the MPP due to variations in climatic conditions. With the value of ΔD(k) an accumulator was made in order to obtain the value of the duty cycle. See equation (8): (8) D(k)=D(k−1)+ΔD(k).

Taking into account that the maximum and minimum voltages of the PV module are V_max = 21.7 V and V_min = 14.47 V, and that in addition the output voltage of the dc–dc converter is 12 V; the range of values that the duty cycle can take is defined in equation (9): (9) Dmax=12V14.47V=0.829;Dmin=12V21.7V=0.552.

In the process of fuzzification, triangular membership functions for the inputs and the output were used; while for the defuzificación the centroid defuzzification method was used. Five linguistic labels were used for E(k), CE(k) and ΔD(k): Very Low (VL), Low (L), Neutral (N), High (A) and Very High (VH). Figure 8 shows the membership functions defined for the inputs, with universe of discourse of (−60 to 10) for the error E(k) and (−10 to 10) for the change of the error CE(k).

For the output ΔD(k) the universe of discourse was defined from (−0.01 to 0.01), as can be seen in Figure 9. Table 3 shows the 25 fuzzy if-then rules that were defined for the fuzzy controller.

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Figure 8:

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In order to compare the results obtained with the fuzzy controller, the P&O algorithm was implemented; which consists in modifying the percentage of the duty cycle of the PWM signal that is responsible for driving the dc–dc converter. In this way, it is possible to modify the operating point in the P–V curve of the PV module. This controller was programmed taking into account four cases: (see Fig. 10).

Case 1: power increase ΔP = P₂ −P₁>0 and voltage increase ΔV = V₂−V₁>0. Action taken by the controller: decrease the duty cycle ΔD.

Case 2: decrease in power ΔP = P₂−P₁<0 and decrease in voltage ΔV = V₂−V₁<0. Action taken by the controller: decrease the duty cycle ΔD.

Case 3: power decrease ΔP = P₂ − P₁<0 and voltage increase ΔV = V₂ − V₁>0. Action taken by the controller: increase the duty cycle ΔD.

Case 4: increase in power ΔP = P₂ − P₁>0 and decrease in voltage ΔV = V₂ − V₁<0. Action taken by the controller: increase the duty cycle ΔD.

Figure 11 shows the flowchart of the P&O controller that was implemented on the Arduino Mega board.

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Figure 11:

The Adafruit data logger shield was used in order to store the values of current, voltage, power, and operating temperature, with the time and date in which the data were processed. This shield incorporates an SD card interface that allows working in FAT16 or FAT32 formats with memories with a storage capacity of up to 32GB. Additionally, it incorporates a real time clock to keep the time and date even when the Arduino is disconnected. See Figure 12(a).

To visualize the variables measured by the MPPT controller in situ, the Nokia 5110 graphic LCD was used, which is based on the PCD8544 controller for a 48×84 pixel matrix, SPI interface, 3.3 V operating voltage and current of 6 mA. Figure 12(b) shows the LCD used.

Additionally, four LM335 temperature sensors installed in the four corners of the PV module were used. The LM335 has an output voltage temperature coefficient of 10 mV/°C and operates in a temperature range of −40 to 100°C. The values obtained from the four sensors were averaged in order to obtain the operating temperature of the PV module in real time.

In order to regulate the voltage supplied by the PV module to energize the controller with 5 V, the regulator LM7805 was initially used; but there were problems of overheating in the regulator. For this reason, it was decided to regulate the voltage of the PV module in stages, using the LM7812 and LM7805 regulators, thus avoiding the initial overheating problems.

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Figure 13 shows the general circuit diagram that was implemented for the MPPT controller, which is composed of each of the circuits and devices detailed in the previous sections. Figure 14 shows the prototype in operation during the tests carried out.

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Figure 14: