Grid Tied Inverter Matlab Simulink

International Journal of Science, Strategic Management and Technology An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases © Author(s). This work is peer-reviewed, openly published, and permanently archived This article is openly accessible and reusable with proper attribution.

Figure: System Model & Simulation Flow for Grid Tied Inverter Matlab Simulink

Design and Simulation of a 300 MW Grid-Tied Solar Photovoltaic System

Supervisor Name: Mr.V.Yokeswaran

M.Tech. (Autonomous) - Nagapattinam, Tamilnadu,India.

Abstract

Solar energy has become one of the most promising renewable sources to meet growing power demands while reducing environmental impact. This paper presents the complete design and simulation of a large-scale 300 MW grid-tied solar photovoltaic (PV) system. The system uses a three- phase four-leg inverter, which offers better performance under unbalanced conditions compared to traditional three-leg inverters.

Figure: System Model & Simulation Flow for Grid Tied Inverter Matlab Simulink

The PV array is modeled considering variations in solar irradiance and temperature. A DC-DC boost converter with Perturb and Observe (P&O) maximum power point tracking (MPPT) extracts maximum power from the panels. The four-leg inverter converts DC power to AC and synchronizes it with the utility grid using a phase-locked loop (PLL). The entire model is developed and tested in MATLAB/Simulink environment.

Figure: System Model & Simulation Flow for Grid Tied Inverter Matlab Simulink

Simulation results show stable DC-link voltage, smooth active power injection into the grid, and low total harmonic distortion (THD) in the output currents. The four-leg topology helps manage neutral currents effectively during unbalanced loads or grid disturbances. This work highlights how the four- leg inverter can improve power quality and reliability in high-capacity solar plants. The proposed design can serve as a useful reference for practical implementation of utility-scale PV systems.

Figure: System Model & Simulation Flow for Grid Tied Inverter Matlab Simulink

Keywords: Solar PV system, Grid-tied inverter, Four-leg inverter, MPPT, MATLAB/Simulink, Power quality, Renewable

1. Introduction

In recent years, the world has seen a rapid increase in the installation of solar photovoltaic systems due to falling panel costs and government support for clean energy. Large-scale grid-tied PV plants in the range of hundreds of megawatts are now common in many countries, including India. These systems not only generate clean electricity but also help in reducing dependence on fossil fuels.

Figure: System Model & Simulation Flow for Grid Tied Inverter Matlab Simulink

However, integrating such high-power solar plants with the existing grid brings several challenges. Solar output varies

Https://Doi.Org/10.55041/Ijsmt.V2I5.138

Cite this Article: V.Yokeswaran, , Roubhan.M, M., Sakthivel.S, , Velmurugan.S, & Ranjith.R, (2026). Design and Simulation of a 300 MW Grid-Tied Solar Photovoltaic System using Four-Leg Inverter in Matlab/Simulink. International Journal of Science, Strategic Management and Technology, 02(05).

License:

This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), permitting use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited. International Journal of Science, Strategic Management and Technology An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases © Author(s). This work is peer-reviewed, openly published, and permanently archived This article is openly accessible and reusable with proper attribution.

Figure: System Model & Simulation Flow for Grid Tied Inverter Matlab Simulink

with weather conditions, and the inverters must maintain good power quality, handle reactive power, and stay stable during grid disturbances. Traditional three-leg inverters often face difficulties when the load or grid becomes unbalanced, leading to neutral current flow, voltage distortions, and higher losses.

Figure: System Model & Simulation Flow for Grid Tied Inverter Matlab Simulink

To address these issues, this project focuses on the use of a four-leg inverter topology for a 300 MW grid-tied solar PV system. The extra leg connected to the neutral point allows better control over zero-sequence components. The complete system was modeled and simulated using MATLAB/Simulink, which is a widely used tool for power electronics and renewable energy studies. This paper discusses the problem, objectives, system design, working principle, methodology, advantages, and key findings.

Figure: System Model & Simulation Flow for Grid Tied Inverter Matlab Simulink

2. Problem Statement

Although solar PV technology has advanced significantly, large grid-tied systems still encounter problems related to power quality and stability. Conventional three-phase inverters can inject harmonics into the grid and struggle with unbalanced conditions commonly found in distribution networks or during partial shading/faults in PV arrays. Neutral currents in unbalanced situations can cause overheating of conductors, voltage imbalances, and even tripping of protection devices.

Figure: System Model & Simulation Flow for Grid Tied Inverter Matlab Simulink

Moreover, achieving efficient maximum power extraction under varying irradiance and temperature while maintaining synchronized grid connection requires robust control strategies. For a high- capacity system like 300 MW, these issues get amplified, potentially affecting overall system efficiency and compliance with grid codes. The main problem addressed in this work is to design a reliable inverter topology and control scheme that can overcome these limitations in a utility-scale solar PV plant.

The Primary Objectives Of This Project Are:

To design a 300 MW solar PV array and model its characteristics under different environmental conditions in MATLAB/Simulink. To implement a DC-DC boost converter with P&O MPPT algorithm for maximum power extraction.

To develop a three-phase four-leg inverter with suitable modulation and control techniques for grid synchronization. To simulate the complete grid-tied system and analyze its performance in terms of power quality, voltage regulation, and response to dynamic changes.

To evaluate the advantages of the four-leg inverter over conventional topologies, especially under unbalanced conditions. 4. Proposed System – Working Principle and Methodology The proposed system consists of three main stages: the PV array with MPPT, the DC-DC converter, and the four-leg grid- tied inverter.

Pv Array Modeling:

The 300 MW PV array was built by connecting multiple PV modules in series and parallel combinations. Each module is modeled using the single-diode equivalent circuit, with inputs for solar irradiance (G) and cell temperature (T). This allows realistic simulation of power output variations throughout the day.

Dc-Dc Boost Converter And Mppt:

A boost converter steps up the variable DC voltage from the PV array to a constant DC-link voltage suitable for the inverter. The Perturb and Observe (P&O) algorithm continuously adjusts the duty cycle of the converter by making small changes and observing the resulting power. This method is simple yet effective for tracking the maximum power point even when irradiance changes suddenly.

Four-Leg Inverter Topology And Control:

The heart of the system is the three-phase four-leg voltage source inverter (VSI). It uses eight switches (typically IGBTs with diodes). The fourth leg provides a neutral connection, which is the key difference from standard three-leg inverters. This extra leg helps independently control the zero- sequence currents.

Sinusoidal pulse width modulation (SPWM) or space vector modulation is used to generate switching pulses. A synchronous reference frame phase-locked loop (SRF-PLL) detects the grid phase angle for proper synchronization. PI controllers regulate the DC-link voltage and grid currents to achieve near- unity power factor and controlled active/reactive power flow. An LCL filter is placed at the output to reduce switching harmonics before connecting to the grid (modeled at 33 kV or 110 kV level, stepped up through a transformer).

The entire system was assembled as a single Simulink model using SimPowerSystems toolbox. Discrete solver with small time steps was chosen for accurate power electronics simulation. Different test cases were run, including standard test conditions (1000 W/m, 25°C), step changes in irradiance, and unbalanced load scenarios.

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.

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

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).

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

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.

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

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).

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

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.

Figure: System Model & Simulation Flow for Ansys Airbag Injury Simulation

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

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