Pv System With Mppt Matlab

Mppt In Solar Pv Systems

Luiz Fernando M. Arruda, Student Membership, Mois´es Ferber, Membership,

Post Conference Paper

Abstract—This article presents a study on the applica- tion of artificial neural networks (ANNs) for maximum power point tracking (MPPT) in photovoltaic (PV) systems using low-cost pyranometer sensors. The proposed approach in- tegrates pyranometers, temperature sensors, and an ANN to estimate the duty cycle of a DC/DC converter, enabling the system to consistently operate at its maximum power point. The strategy was implemented in the local control of a ´Cuk converter and experimentally validated against the conventional Perturb and Observe (P&O) method. Results demonstrate that the ANN-based technique, leveraging af- fordable sensor technology, achieves accurate MPPT per- formance with reduced fluctuations, enhancing the respon- siveness and efficiency of PV tracking systems.

Index Terms—Photovoltaic Systems, MPPT, Artificial Neural Networks, Low-cost Sensors, ´Cuk Converter, Solar Energy.

I. Introduction

Solar energy is one of the fastest-growing sectors in the global energy landscape, having experienced a remarkable 1135.15% increase from 2013 to 2023. Despite this growth, integrating photovoltaic (PV) systems into the electrical grid remains challenging. Key issues include weather dependence, power conversion losses, grid instability, and the complexity of grid synchronization . Additional challenges involve maxi- mum power point tracking (MPPT) under dynamic conditions and partial shading caused by clouds, dust , and physical obstructions such as buildings and trees .

To address these challenges, a variety of MPPT algorithms have been developed to improve the efficiency of PV modules under rapidly changing weather and irradiance conditions.

These techniques can be classified into classical, hybrid, optimal, and intelligent methods . Classical approaches, such as Constant Voltage (CV), Incremental Conductance (IC), Open-Circuit Voltage (OCV), Short-Circuit Current (SCC), Hill-Climbing (HC), Perturb and Observe (P&O), Modified P&O, and Adaptive Reference Voltage (ARV), are relatively simple to implement in embedded systems and rely on voltage and/or current measurements .

The application of artificial intelligence (AI) in solving elec- trical engineering problems has become increasingly common, particularly due to advances in embedded computing and mi- crocontroller technologies. Notable examples include adaptive parameter optimization in STATCOMs using ANNs , data- driven energy management in plug-in hybrid electric vehi- cles , fusion-based dynamic modeling of proton exchange membrane fuel cells , and AI-assisted control strategies for enhancing transient response in power-electronic-dominated grids , .

Several studies have investigated AI-based MPPT tech- niques –, each proposing distinct methodologies to estimate the optimal operating point. In this context, the present work introduces an ANN-based MPPT strategy that uses temperature and solar irradiance measurements from a low-cost sensor to predict the ideal duty cycle of a power converter.

The main contributions of this work are summarized as

Follows:

• The use of a low-cost pyranometer for real-time solar

Irradiance Estimation;

• The application of artificial neural networks to directly estimate the duty cycle in power electronic converters,

Eliminating The Need For Perturbative Methods;

• An experimental comparison between the conventional

P&O Method And The Proposed Ann-Based Mppt Tech-

nique using low-cost sensors. To validate the proposed methodology, it was implemented in C and embedded into a ´Cuk DC/DC converter. Experimental tests were conducted to evaluate and compare its performance.

The remainder of this paper is organized as follows. Sec- tion II presents an overview of artificial neural networks (ANNs). Section III describes the implementation of the proposed methodology. Section IV discusses the experimental results. Finally, Section V presents the conclusions.

Ii. Ann Methodology

The core concept of the proposed methodology relies on using a low-cost pyranometer sensor combined with an arti- ficial neural network (ANN). ANNs act as universal function approximators and are highly effective in solving complex nonlinear problems.

A model of an artificial neuron is shown in Figure 1. In this study, the artificial neuron employs the generalized delta rule for weight adjustment –, which assumes that the error is directly measurable, thus enabling each synaptic weight to be updated individually.

Fig. 1: Artificial Neural Model

Similar to a biological neuron, Xm represents the input stimuli, wkm the synaptic weights, and dk(n) the desired output. The synaptic weight determines the importance of each input signal in the processing. The activation value of the

(1)

A bias value bk is then added to modify the activation

(2)

The neuron output yk is obtained by applying a nonlinear

(3)

This activation function limits the output to a finite range. In this work, the hyperbolic tangent function is used:

(4)

The training of an ANN involves adjusting the network weights based on a dataset composed of known input-output pairs. The objective is to make the output yk as close as possible to the desired output dk .

The forward propagation of inputs through the network layers to the output is known as the feed-forward process. For each training instance, the error ek is computed as:

(5)

The mean squared error (MSE), used as a performance

(6)

The learning process occurs via backpropagation, where the error is propagated backward through the network layers to update the synaptic weights. The local gradient δ(n) for each

(7)

Since the activation function is the hyperbolic tangent, its

(8)

Using the generalized delta rule, each synaptic weight is

(9)

where α is the learning rate. To ensure generalization, the dataset is divided into two subsets: a training set (typically 70% of the data) and a validation set (the remaining 30%). The selection of the neural network architecture in this work was performed through trial-and-error analysis. Various architectures were evaluated using different activation functions, including GELU, ReLU, SELU, sigmoid, softmax, softplus, softsign, and swish. The configuration with the best generalization capability or lowest mean squared error was selected for implementation.

Fig. 2: Multilayer Perceptron

Figure 2 illustrates a multilayer perceptron (MLP), which consists of multiple layers of neurons. Each neuron processes and propagates its output, along with the bias, to the neurons in the next layer. This process continues layer by layer until the final (output) layer is reached. An MLP structure was adopted in this work.

Algorithm 1 summarizes the training and validation proce- dure. It outlines a systematic approach that begins with dataset preparation and splitting. The process involves iterative testing of different ANN configurations to identify the most accurate model. The selected ANN’s weights are then exported and embedded in the control system, ensuring practical deployment in a microcontroller environment.

Electronic Converter

This section is divided into three parts: the low-cost pyra- nometer description, dataset creation, and neural network training.

1: Start

2: Create Dataset (Solar Irradiation, Temperature, Duty

Cycle)

3: Split Dataset into Training and Validation sets

6:

Choose an ANN architecture and activation function

A. Low-Cost Pyranometer

Fig. 3: Low-cost solar irradiance sensor RS-RA-V05-JT The low-cost sensor used in this study is the RA-T5-V05- JT, shown in Fig. 3. It converts solar irradiance into an output voltage signal and, when used with the ANN, represents a key innovation of this work. By using geographical parame- ters (latitude, longitude, timezone, and day of the year) and physical data (solar irradiance, Sun radius, Earth-Sun distance, air mass, ozone layer absorption, and direct irradiance), it is possible to estimate solar irradiance throughout the day. This estimation enables proper sensor calibration .

TABLE I: Geographic and Physical Data for Estimating Solar

70%

Using these parameters and a data logger, Fig. 4 shows the estimated solar irradiance (in W/m2) and the sensor output voltage throughout the day. The x-axis represents the time (from 06:00 to 16:00), the left y-axis shows estimated irradiance, and the right y-axis shows the sensor voltage.

The blue curve indicates the estimated irradiance, following a typical diurnal solar pattern: starting at zero before sunrise, peaking near noon, and decreasing toward sunset. The red curve represents the sensor response, which generally follows the same pattern but with noticeable fluctuations, possibly due to shading, cloud cover, or sensor noise. Between 08:30 and 15:30, partial shading from nearby buildings significantly reduces the sensor output.

Hour

Fig. 4: Comparison between Estimated Irradiance and Sensor

B. Dataset Creation

To obtain reliable data that captures system behavior un- der various conditions, a data logger is typically used in a photovoltaic (PV) system to monitor key variables. The main environmental inputs affecting the PV system operating point are solar irradiance and temperature. Combined with the construction data of the PV panel, it is possible to estimate voltage, current, and power at the maximum power point

36

In this work, mathematical equations representing the be- havior of the YL150P-17B panel (see Table II) were used. Temperature and irradiance data were collected and used to calibrate the pyranometer and generate MPP voltage, current, and power values.

Since the ultimate goal is to predict the duty cycle of a power converter, it was also necessary to include static gain and load parameters. The dataset was constructed using esti- mated voltage and power values, varying resistive loads (1 Ω to 19 Ωin steps of 2 Ω), and calculating the corresponding duty cycle for a ´Cuk converter. The dataset spans irradiance

Deployment In Out-Of-Position Situations

D. Bendjaballah1, A. Bouchoucha1, M. L. Sahli1,2* and J-C. Gelin2

Abstract

Side-impact collisions represent the second greatest cause of fatality in motor vehicle accidents. Side-impact airbags have been installed in recent model year vehicle due to its effectiveness in reducing passengers’ injuries and fatality rates. In meeting these requirements, simulations of folding and deploying airbags are very useful and are widely used. The paper presents a simulation method for the deploying airbags using three materials in different working conditions. Finite element analysis is primarily used to evaluate this concept. In these simulations, the gas flow is described by the conservation laws of mass, momentum, and energy. The numerical results indicate that the FE method in this paper is capable of capturing airbag deploying process accurately.

Keywords: Airbag simulations, Out-of-position, Crash, Modeling, Out-of-position

Background

The passive safety of cars has become a very high prior- ity issue for the automotive industry. Today, there are not only one or two airbags in a car; certain models have ten times more than that. With the increasing usage of airbags, the number of accidents where the airbag itself can cause an injury to the occupant also increases

(Augenstein Et Al. 2003; Gabauer And Gabler 2010;

Audrey et al. 2011). As is well known, safety belts are also now devices designed to provide protection to the users of vehicles during crash events, minimizing the loads necessary to adapt their movement to the move- ment of the car (Freesmeier and Butler 1999; Schmitt et al. 1997). In general, the seat belt is designed to restrain the occupant in the vehicle and prevent the

Occupant From Having Harsh Contacts With Interior

surfaces of the vehicles. The airbag acts to cushion any impact with vehicle structure and has positive internal pressure, which can exert distributed restraining forces over the head and face. As a safety component of auto- mobile, an airbag decreases occupants’ injury likelihood effectively in case of an accident (Ruff et al. 2007). These safety elements can reduce the death rates on the roads, and its protection effects have been widely approved (Crandall et al. 2001; Teru and Ishikawa 2003). With computational tools such as finite element methods designed for dynamic contact problems, crashworthiness simulations can now be used with reliable accuracy to evaluate occupant protection in various collision condi- tions with safety metric/parameters such as acceleration, head injury criteria, intrusion distance, intrusion vel- ocity, and neck forces (neck injury risk or whiplash).

Thus, new types of airbag products are being developed to handle different collision scenarios.

Become Standard Equipment On Most New Passenger

vehicles (Braver and Kyrychenko 2004; Teng et al. 2007; Yoganandan et al. 2007). The airbag cushion is com- posed of a woven fabric which is rapidly inflated during a car crash. The airbag dissipates the passenger’s kinetic energy thereby reducing injury through biaxial stretching of the fabric bag and escaping gas through vents. There- fore, the performance of the airbag is greatly influenced by the mechanical properties of the fabric. Generally, air bags are designed to deploy in a crash that is equivalent to a vehicle crashing into a solid wall at 8 to 14 mph.

Air bags most often deploy when a vehicle collides with another vehicle or with a solid object like a tree. There are various types of airbags: frontal, side-impact, and curtain airbags. In general, the passenger side airbags are usually larger than the driver airbags (see Fig. 1).

Besançon, France

© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Bendjaballah et al. International Journal of Mechanical

Doi 10.1186/S40712-016-0070-2

Extensive studies have shown that the airbag deploy- ment in load cases consists of two occupant loading phases: a punch-out effect where the airbag bursts out of its container with the airbag and airbag module cover accelerating towards the occupant and a second loading phase during which the airbag is taking on its deployed shape and volume (membrane-loading effect). Bankdak et al. (2002) developed an experimental airbag test system to study airbag-occupant interactions during close proximity deployment. The results provided insight for simulating the effect of inflation energy and mass flow on target response. Bedard et al. (2002) found that while left-side (driver-side) impacts accounted for only 13.5% of all crashes, the fatality rate among these

Crashes Was 68.3% In Comparison To Front Impact

(48.3%), right-side impact (31.3%), and rear impact (38.4%). These studies underscore the importance of oc- cupant safety during side-impact collisions. In the last years, the current market requested to reduce the time and cost airbag development. In order to achieve this result, virtual simulations play an important role since they allow to minimize the number of experimental tests (Pei et al. 2013; Cao et al. 2014). Several simulation models of airbag were established (Wang et al. 2007). It is feasible to optimize the parameters of airbag deploy- ment using simulation technology. Experimental and numerical studies have quantified injury risks to close- proximity occupants from deploying side airbags. These studies have focused on the prevention of the most ad- verse effects of airbag deployment (Duma et al. 2003).

Other studies have proposed airbag characteristics to minimize particular biomechanical responses (Haland and Pipkorn 1996). In a more recent study, Marklund and Nilsson (2003) compared deformation patterns with experimental data as well as the computational costs associated with three different airbag deployment simu- lation methods; they concluded that the SPH method is relatively inexpensive and produces incremental deform- ation patterns that compare most closely to the experi- mental results. The process of inflation of an airbag is one of the determining factors in saving lives. The duration from the initial impact of the crash to the full inflation of an airbag is about 40 ms, and during this time, the airbag goes from being in a folded state to a fully inflated state, with a high internal pressure. After achieving this state, the airbag begins to deflate, thus providing a nice cushion for the body impacting it.

Ideally, the person in the crash should come into contact with the airbag at this time. In the present study, a large volume passenger side airbag model is developed to handle different collision scenarios. The main aim is evaluate the performance of deploying of passenger side airbag using finite element methods (FEM).

Materials

The tensile specimens were made in different airbags (P: Peugeot, R: Renault, and VW: Volkswagen) with a length of 200 mm long and a width of 40 mm. Table 1 shows the mechanical properties of the airbag.

Tensile Tests

To determine the mechanical properties of the material of airbag used in the test pieces, tensile tests were performed on Lloyd EZ20 universal testing machine in Constantine. These tests were conducted using rect- angular samples. The axial force and axial displacement acquired during a test are converted into stress and the strain in order to be used for the fabric material model.

The continuous recording of the stress-strain data was performed during both the load and unload phases. A minimum of five samples were made in order to check the repeatability of the measurements. All the data was collected by using a PC-based data acquisition system and analyzed by commercial software. The picture frame test device that is made for this study is shown in Fig. 2.

Fig. 1 a Frontal and side airbags. b Oblique view of facet occupant model in sitting posture following airbag deployment (Lim et al. 2014)

0.150

Bendjaballah et al. International Journal of Mechanical and Materials Engineering (2017) 12:12

Page 2 Of 9

Figure 3 shows the stress-strain relationship of the airbag sample under axial tensile loads. The results are showing a linear increase in extension with the increas- ing stresses. This is an expected output and it confirms with the theoretical behavior of a sample subjected to tensile stress. The rupture strain values for different airbags (R/P/VW) were 0.322, 0.441, and 0.472, respect- ively. The measured elastic parameters (i.e., Young’s modulus E and initial yield strength) and Poisson’s ratio are summarized in Table 2. The tensile tests of the woven fabrics can show differences on mechanical prop- erties because woven fabrics can resist in-plane shear loads once the yarn lock-up angle has been reached. The differences of material property on material direction can affect the shape of fully deployed bag (see Fig. 3b).

Theoretical Background

Numerical simulations of airbags use very complex and techniques such as an orthotropic model to identify the mechanical behaviors during the airbag inflation and the fluid mechanics (gas flow) to describe the inflator gas flow (pressure gradient) and improve the representation of the pressures within the airbag. To model the airbag as an orthotropic model, three material constants have to be provided. Assuming a plane stress condition, the

Ð1Þ

where σ is the normal stress and τ is the shear stress, the subscript refers to the principal material directions, i.e., the fill and warp directions. Also, ε and γ are the strain components. The material elastic constants Qij are

Ð2Þ

where E1 and E2 are the Young’s modulus in the fill and wrap directions and G12 is the shear modulus of the fabric material. νij is the Poisson ratio of the material.

The gas exerts a pressure load on the airbag causing it to expand. This expansion puts the airbag under tensile stress lowering the expansion rate. In this study, heat conduction and heat transfer is not taken into account.

Fig. 2 A photograph of Lloyd EZ20 universal testing Fig. 3 Stress versus strain using Lloyd EZ20 machine for a three different airbags at 0° and 90° and b VW airbag test specimens at

Different Angles

Table 2 Physical and mechanical properties of the airbag

Page 3 Of 9

In the deployment of an airbag, an inflator supplies high velocity gas into an airbag causing it to expand rapidly. The gas inside the airbag is assumed to be ideal, to be of constant entropy, and to satisfy the equation of state:

Ð3Þ

Here p, ρ, and e are respectively the pressure, density, and specific internal energy, and γ is the ratio of the heat capacities of the gas. The gas flow is described by the conservation laws for mass, momentum, and energy that

Ð4Þ

here, V is a volume, A is the boundary of this volume,

N Is The Normal Vector Along The Surface A, And U

denotes the velocity vector in the volume. Applying Bernoulli’s equation in the case of an ideal gas with

Ð5Þ

Here, the subscript ex denotes quantities at the throat of the tube. Furthermore u, p, and ρ denote the quan- tities inside that part of the tube that is supplying mass.

Materials And Boundary Conditions

The airbag system mainly consists of three parts: the airbag itself, the inflator unit, and the crash sensor or diagnostic unit. Thus, to study the behavior of the airbag using FE simulations, we need to have an FE model of the airbag in the folded position. A FE model of the airbag was used to simulate the test condition as shown in Fig. 5. LS-DYNA® material model FABRIC (MAT_34) is used to simulate the airbag material. It is a variation of the layered orthotropic material model. Additionally, in the LS-DYNA® material model, fabric leakage can be accounted for. However, for this CAB material, the leak- age is almost negligible and therefore no leakage is specified. The mechanical properties can be determined from the physical test. Typical material properties for airbag fabrics are taken as given in Chawla et al. (2004a) (Table 3). These properties are used to simulate inflation process of airbag (see Table 1). The car dashboard is modeled as the rectangular thin plate using a MAT_RI-

Gid Material, And The Degrees Of Freedom Are Con-

strained in all the directions. The similar properties of thermoplastic polymer are assigned for contact purposes. The porosity of the fabric is assumed zero. The nitro- gen gas is taken for inflating the airbag. Properties of nitrogen gas and initial bag conditions are shown in Table 4. The example on which we perform the study is a typical passenger side airbag. The geometric de- tails have been measured from a commercially avail- able airbag. The initial state of the airbag is a closed rectangular whose sides are to be finished to 482 × 635 mm2 and is shown in Fig. 4.

Table 3 Material properties of airbag and rigid plate used in FE

–

Table 4 Initial values used for FE simulation of the swelling of

3.33 × 10−4

Fig. 4 The initial airbag geometry in the form of a rectangular Bendjaballah et al. International Journal of Mechanical and Materials Engineering (2017) 12:12

Related Journal Articles & DOI Links

Selected peer-reviewed publications relevant to 12 Lead ECG Acquisition. Click the DOI to access the full paper (may require institutional access).

Why Choose Us?

Bangalore guidance for robotics, Spectre and autonomous systems projects.