Design of MPPT Algorithms using Simulink Support Package for Arduino Hardware

In this paper, the performance of the DC-DC converter is assisted by MPPT control in a comparison between Perturb & Observe (P&O) and Incremental Conductance (IC) using the Simulink Support Package for Arduino Hardware. In photovoltaic systems, the maximum power point tracking needs to be powered by PV panels. A microcontroller controls the power converter. Before it is designed, the sys-tem is first modeled and simulated using MATLAB/Simulink. The control software implements a pulse-width modulation signal generator embedded in the Arduino microcontroller. The many stages of design for these controllers are discussed, along with simulation and experimentation. The system is tested, and the irradiance varies from 500 w/m 2 to 1000 w/m 2 . The results of this simulation and experiment show that the plan with MPPT and a P&O controller has a higher efficiency of PV power production.


Introduction
The fossil-gasoline-based, completely traditional power era is unsustainable. This has been one of the primary motivators for the considerable incorporation of renewable energy sources, including wind, solar photovoltaic (PV), hydropower, biomass electricity, geothermal energy, and ocean power, into public networks throughout the previous decade. Solar PV power output has persisted in developing excessively as one of the leading renewables in the last few years. It now occupies a large position in the energy era. The maximum power point tracking (MPPT) approach allows photovoltaic (PV) systems to maximize energy production under fluctuating sun irradiation and ambient temperature conditions. As a result, the overall efficiency of the PV energy-producing system increases. The PV module's power-voltage characteristic has a derivative, which is the foundation for the perturbation and observation (P&O) MPPT approach. The output voltage and current of the PV module or array are often sampled at successive sampling stages throughout the P&O MPPT process to determine the related output power and the power derivative with voltage [1][2][3][4][5]. The MPPT technique was implemented by modifying the signal of the power converter PWM controller. The P&O MPPT method, based on the procedure described in [1,4,5], can be executed using a control unit microcontroller or DSP device. This phase is repeated until the gradient value reaches a predetermined threshold, indicating that convergence close to the MPP with the requisite precision has been achieved. This study provides a photovoltaic (PV) model for designing PV systems with a basic MPPT to achieve low-cost design, better performance, and high efficiency. Verification and validation are carried out using an experimental platform based on an Arduino Uno board. To validate this solution, a hardware test bench is built utilizing the Arduino Uno board's low-cost ATMega328 microprocessor. Significant cost savings have been achieved, demonstrating the proposed controller's financial competitiveness.
Where Is: Diode saturation current, a: Diode ideality constant, Vd: Diode voltage, and Vt: Thermal voltage of the semiconductor junction, the temperature-dependent diode saturation current is defined in equation (3).
An output current and voltage (I-V) curve is commonly used to explain the properties of a PV cell. Fig. 2 shows the I-V curve using the PV cell parameters from Table 1. Power production is one of the essential measured values when running a PV cell. The output power can be estimated by multiplying the output current and voltage from the I-V curve, as illustrated in the same image.
The PV cell has the characteristics of a voltage source on the right side of the I-V curve, with an almost constant output voltage. On the other hand, the current source behavior is evident on the left hand of the I-V curve. There is a point at which the output power is maximized. This is known as a Maximum PowerPoint (MPP). Due to the PV cell's low efficiency (at the moment, 25%), PV systems must continuously operate close to the MPP to maximize energy gathering. As shown in

Boost Converter Modelling
Consider a PV panel with a resistive load coupled to a boost-type converter. The converter's input and output voltages have the following relationship: Where Vin is the input voltage, D is the duty ratio, and Vout is the output voltage. The power delivered from this PV panel, assuming the converter runs at 100% efficiency with a resistive load RL, would be The duty ratio command of the converter might be correctly adjusted to produce the maximum power. To operate the converter in continuous conducting mode, certain converter characteristics were chosen. The criteria chosen for this study are Cin=1000µF, Cout=1000µF, L=220 µH, load resistance of 150Ω, and switching frequency is 980 Hz.
(a) As a function of irradiation (b) As a function of temperature

Methods
By progressively changing the DC-DC converter's duty ratio, MPPT algorithms maximize power production. There have been numerous developments and applications of MPPT techniques, including P&O (Perturb and Observing) and IC (Incremental Conductance) [6-10].

P&O Algorithm
The most common and widely utilized method of tracking the MPP is P&O. The flowchart of the P&O MPPT technique is shown in Figure 3. The idea behind this approach is to mess with the system's output voltage, which will mess with the generated power. We proceed in the direction of perturbation until we find the maximum power (when ∆P/∆V = 0), if the new power is greater than the old. If this is not the case, we modify the direction of the perturbation to discover the maximum power.

Incremental Conductance (IC) algorithm
The incremental conductance (IC) method solves the drawback of the perturb and observes the process of tracking peak power under rapidly changing atmospheric conditions. This approach can identify whether the MPPT has reached the MPP and stops agitating the operating point. If this criterion is not satisfied, it is possible to determine the direction of the MPPT operating point's perturbation by analyzing the relationship between ∆I/∆V and -I/V. The fact that ∆P/∆V is negative when the MPPT is to the right of the MPP and positive when the MPPT is to the left of the MPP leads to this relationship. When the MPPT reaches the MPP, this algorithm decides when the P&O oscillates around the MPP. This is undoubtedly a benefit over P&O. Furthermore, incremental conductance is more accurate than the perturb and observe the method in tracking quickly increasing and decreasing irradiance circumstances. The technique is understood by the flow chart displayed in Figure 4.

P&O and IC Algorithm Design With Simulink Support Package
In this section, P&O and IC algorithms are designed with the Simulink support package for Arduino hardware. The Simulink Support Package for Arduino Hardware supports the creation and execution of Simulink models on Arduino boards. A library of Simulink blocks for configuring and accessing Arduino sensors, actuators, and communication interfaces is included in the support package [15]. Figure 5 shows the design of MPPT-based P&O and IC algorithms. Both algorithms were designed based on the algorithms in Figure 3 and Figure  4 by using the s-function builder. To read the voltage and current from the PV module, the voltage sensor and current sensor are used. The voltage sensor is read by Analog pin A1, while the current sensor is read from pin A0 of Arduino hardware. The voltage and current are needed to be converted, then sent as a value to the P&O or IC algorithm for calculation. The PWM signal is generated using pin 6 with a frequency of 980 Hz. The power is measured and sent via serial transmission to display the value of power that obtain by the MPPT algorithm.

Result and Discussion
In this part, we show the simulation results on MATLAB Simulink. The system comprises a PV panel, DC/DC boost converter, MPPT (P&O, IC) controller, and load. This system will be tested with different variations in irradiation and temperature. Figure 6(a) shows changing temperature levels from 20 o C to 25 o C, 25 o C to 40 o C, and 40 o C down to 30 o C for comparison of P&O and IC algorithm performance. Figure 6(b) shows changing levels of irradiation from 1000W/m 2 down to 500W/m 2 , 500W/m 2 to 700W/m 2 , and 700W/m 2 to 1000W/m 2 . Figure 6(c) shows the power generated by using P&O and IC algorithms. As a result of these figures, the performance and effectiveness of P&O and IC algorithms have been demonstrated. The maximum power operating voltage point can be tracked by P&O and IC MPPT. The P&O must be chosen for practical application due to its superior performance over the IC controller. As a result, the P&O has greater performance and is closer to the IC. In this section, we will use the Simulink Support Package for Arduino Hardware to perform experimental validation of the P&O and IC MPPT algorithms. The entire test bench is divided into three groups, which are as follows: (1) PV panel of 390 watts, DC/DC boost converter, resistance load; (2) Arduino Uno for control unit; (3) measuring by a computer equipped with MATLAB/Simulink.

Hardware Setup
The main controller board is the Arduino Uno, which is based on the ATmega328P. It contains 14 digital I/O pins (six of which are PWM outputs), 6 analog inputs, a 16 MHz ceramic resonator, a USB connection, a power jack, an ICSP header, and a reset button. Figure 7 shows the Arduino Uno development board. (c) Figure 8 shows a block diagram of the proposed control board. For current measurement, we used the ACS712-20A sensor. This ACS721 current module is based on the ACS712 sensor, which can detect AC or DC current correctly. The maximum AC or DC that may be detected is 20A, and the current signal can be read using the Arduino's analog input/output interface. As the DC Sensing Unit, a basic voltage divider circuit is utilized to convert the input DC voltages to a DC voltage in the 0 to 5 V range. This scaled-down voltage can be read by the Processor Unit and used to calculate the actual DC voltages. In this experiment, the voltage sensor is designed with a first resistor of 1MΩ and a second resistor of 10K that can measure DC input from 0 up to 500V. A boost converter is installed between a PV panel and a load. The experimental testing as shown in Figure

Experimental Result
The experimental test is performed to find the maximum power point (MPP). To monitor the signal, first go to MATLAB Simulink's model settings and select Hardware Implementation. On the hardware board, choose the Arduino Uno, and then, on the target hardware resources, set the host COM port to "automatically." Then, go back to hardware and click Monitor &Tune. Figure 10 shows the power produced by a PV panel of about 186.65W using the P&O and IC controller. According to the power plots, the P&O technique exceeds the IC strategy regarding steady-state performance, particularly regarding ripple around the maximum point. The power generated by the P&O approach is more significant than that produced by the IC method. Figure 11 shows the voltage of the PV panel. Figure 12 shows the current of the PV panel read by the ACS712 current sensor.

Arduino Uno Load
Voltage and Current Sensor Computer with MATLAB/Simulink DC/DC Boost Converter

Conclusion
In this research, we presented two methods for monitoring a PV panel's MPP. The system was tested and designed to show the benefits of the P&O method over the IC method, and its performances were analyzed through simulation and experimental implementation. The P&O MPPT demonstrates its sensitivity to atmospheric changes and provides a quick response time with a very low relative ripple rate. This experiment shows the capability of P&O and IC algorithms on the Arduino Uno board for real-time monitoring and application using the Simulink support package for Arduino hardware. Furthermore, this research presents the way for fast, easy, and customized control design.