Dq Control Inverter Matlab

International Journal of Applied Power Engineering (IJAPE)

Journal Homepage: Http://Ijape.Iaescore.Com

Optimized control strategy for a three-phase grid connected

Inverter Using Pi Controller And Dq Frame

Nagasridhar Arise, Madde Saiteja, V. Siddu, Vadluri Kavya, Mada Vijay

Accepted Apr 26, 2024

This paper provides a proportional-integral (PI) controller and direct- quadrature (DQ) frame transformation-based optimum control method for a three-phase grid-connected inverter. In terms of grid synchronization, voltage regulation, and harmonic abatement, the proposed control technique attempts to improve the inverter's performance. By separating the control of active and reactive power, the control structure is made simpler and independent regulation of these parameters is possible. This improves the inverter's capacity to quickly react to grid disruptions and track reference values accurately. In order to lower carbon emissions and improve grid dependability, it has become vital to integrate renewable energy sources into the current power grid. Grid-connected inverters are essential in this situation because they transform DC electricity from renewable sources into grid-safe AC power. This abstract outline a proportional-integral (PI) controller and direct-quadrature (DQ) frame-based optimal control method for a three-phase grid-connected inverter using a MATLAB simulation.

Three Phase Inverters

This is an open access article under the CC BY-SA license.

Hyderabad, India

1.

Introduction

A crucial component of the global shift to sustainable energy systems is the quick development of renewable energy sources like wind power. The usage of three-phase LCL-type grid-connected inverters is one of the favored methods for integrating renewable energy installations like wind turbines into the public grid.

These inverters make it easier to transform variable renewable energy output into a format that can be fed into the grid. Nevertheless, despite their extensive use, these inverters' interface with the grid has inherent stability issues. As a result, it is critical to carefully examine and fix these stability issues . The pursuit of energy efficiency and sustainability is critical in today's society across many businesses. Elevator systems are one key area where energy-saving technology is crucial, particularly with the use of three-phase grid-connected inverters. With the use of this technology, extra DC energy may be converted into AC power and easily incorporated into the power grid, effectively acting as a load . The installation of a grid-connected three- phase inverter system while abiding by industry norms and criteria is the main topic of this article. Average models are used to represent the inverter and LCL filter, and the design of the control scheme in the DQ reference frame is discussed . In order to overcome these difficulties, a proportional-resonant (PR) controller in a stationary coordinate system is offered as a novel solution in this study. Infinite control gain at a particular resonance frequency is one of the PR controller's standout traits. At that frequency point, steady-state error- free control can be achieved thanks to this special characteristic . This study addresses the challenging problems of power coupling and intricate decoupling in the context of a three-phase grid-connected inverter's

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Optimized control strategy for a three-phase grid connected inverter using … (Nagasridhar Arise)

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d-q coordinate system . The gains of the proportional-integral (PI) controllers are improved utilizing a genetic algorithm (GA) through an adaptive online tuning procedure to guarantee the adaptability and efficacy of the control system . An external power loop and an internal current control loop, both implemented using customary PI type controllers, are two crucial parts of the suggested control method . The power generated travels via a three-phase inverter on its way from generation to consumption, operating in accordance with the DQ principle to ensure smooth communication with the grid. This integration not only makes it easier to export excess energy, but it also deals with the crucial problem of poor power quality, which can develop in such intricate systems . These control techniques' main goal is to smoothly transfer all active power produced by non-renewable sources into the grid utility while maintaining the highest standards of power quality and achieving a unity power factor . This paper not only explains the complex design of the controller but also goes into great detail on how it is implemented and evaluates how well it works using a number of simulations .

The voltage source inverter (VSI) that powers the DVR uses two PI controllers to carefully control the insulated gate bipolar transistor (IGBT) pulses. These controllers accomplish this by fine-tuning the D-Q axis voltage signals, with each controller handling a single axis component of the load voltage as input and output . A DC-link voltage control loop using a PI controller is cascaded with an internal current loop using PI and proportional-resonant (PR) controllers in the suggested topology. For non-renewable systems, these control techniques are frequently used in a variety of recognized reference frames. The steady-state performance of the proposed system was determined by evaluating key performance indicators, such as total harmonic distortion . The incorporation of micro-sources into microgrids, however, also presents difficulties, particularly in resolving problems with power quality. Non-linear and unbalanced loads in the distribution system might cause disturbances that degrade the microgrid's overall power quality. This paper evaluates the efficiency of the voltage source inverter (VSI) topology and suggests a method for improving stability in the context of current stochastic grid systems by employing a proportional-integral (PI) controller . Power quality issues in smart microgrid networks are caused by the substantial voltage variations that these grid systems frequently suffer . The performance of these techniques is then carefully compared, taking into account elements like grid voltage disturbance rejection and stability under various grid short circuit levels . Voltage stability has been significantly challenged by the rapid expansion of renewable energy sources, notably solar energy systems connected to low and medium voltage networks . Three crucial performance indices are used to assess the effectiveness of the suggested method: the electrical signal's root mean square (RMS) value, total harmonic distortion (THD), and voltage sag compensation -. The form of a modular transformer less grid-connected photovoltaic multilayer inverter, a novel approach is presented in this work -. In this study, we independently set limits for the d-axis and q-axis grid currents using a thorough nonlinear closed-loop system analysis that is based on input-to-state stability theory -. Our study's simulation results will be presented and discussed, shedding light on the possibilities of integrated renewable energy systems as a sustainable and forward-thinking strategy to fulfil our expanding energy needs while preserving our planet's vulnerable ecosystems .

2.

Synchronous Reference Frame Theory

In Figure 1 shows P-Q control schemes of a three-phase grid connected inverter in a micro grid. In Figure 1, Lf stands for the equivalent inductance of the LC filter, Rf is the equivalent resistance of the LC filter, and Vdc is the DC voltage supplied by a distribution generation unit. Cd and Cf are the capacitance of the DC side and the LC filter, respectively. The grid-side voltage, a current and phase detector, an inverter-side voltage and current detector, a calculation of active and reactive power, an active power PI controller, a reactive power PI controller, a current PI controller, abc/dq and dq/abc transformations, and space vector pulse width modulation (SVPWM) are the key operations that make up the P-Q control scheme. A control method used in power electronics to manage the flow of electrical energy between a microgrid (a localized collection of distributed energy resources) and the primary utility grid is known as the P-Q control system of a three-phase grid-connected inverter. The active power (P) and reactive power (Q) exchanged between the utility grid and the microgrid are the two main parameters that are the subject of this control scheme's attention. An overview

Of The P-Q Control Technique Is Provided Below:

- Active power control (P): The actual work carried out by electrical energy is referred to as active power, also known as real power. The P control in a microgrid makes sure that the inverter can control the transfer of actual power with the utility grid. The inverter modifies its output to deliver active power to the microgrid when the microgrid needs to import power (for instance, during a deficit). On the other hand, the inverter can export extra electricity to the utility grid when the microgrid generates excess power (for instance, from renewable sources like solar panels).

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- Reactive power control (Q): Reactive power is not directly used for labour; rather, it is necessary to keep the grid's voltage levels stable. By using Q control, the inverter may control the flow of reactive power and supply or absorb it as necessary. When an inverter is operating in capacitive mode (supplying reactive power), the microgrid's voltage levels are increased, and when it is operating in inductive mode (absorbing reactive power), the voltage levels are decreased.

Qref

Figure 1. P-Q control schemes of a three-phase grid connected inverter in a micro grid 3.

System Configuration

In Figure 2 shows the block diagram of the reference current extraction of PI controller based on instantaneous reactive power (IRP) theory. In Figure 2 the three phase line currents are a, b and c line current; they are connected to 3-phase to 2-phase Clark’s transformation and it converts 3-phase to 2-phase represented by α and β currents. The Clarks transformation also to use control the three-phase system. The α and β currents are connected to the active and reactive power component and the power of the equations are p and q are connected, it produces the accuracy and it is gives linear system, this PI controller provides a fast response time, the linear active and reactive powers are connected to the line current of α and β currents, and the line currents are connected reverse Clarks transformation by using reverse Clark’s transformation we can convert 2 phases to 3 phase line currents. The line currents are connected to hysteresis based PWM current controller, by using this component to generate controllable frequency and also control ac voltage magnitudes by using pulse width modulation. So, the line currents are connected to voltage source inverter. So, by using filters we can reduce the harmonics. And the three phase voltages are connected input to the three phases to two phase Clark’s transformation in reverse supply, and the three phase to two phase Clark’s transformation and it will give the output in terms of α and β voltages and it connected to the power components.

3.1. Pi Controller

Grid inverters and other control systems frequently use the proportional-integral (PI) controller as a control mechanism. API controller is frequently used in the context of a grid inverter to control the electricity flow between renewable energy sources (like solar or wind power plants) and the grid. To reduce errors and keep an output that is accurate and consistent, it makes use of both proportional and integral components. The controller must be properly tuned to work at its best under various operating settings and grid conditions.

- Proportional component (P): The current error, or the discrepancy between the planned setpoint and the actual process variable, is what the proportional component of the controller reacts to. It attempts to lessen the error by producing an output that is proportionate to the error. The magnitude of this response depends on the proportional gain (Kp).

- Integral component (I): The integral component produces an output that acts to correct any steady-state or long-term errors by accumulating the past error over time. It is crucial for getting rid of any bias or offset in the system. How aggressively the controller responds to accumulated error depends on the integral gain (Ki).

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Figure 2. Block diagram of the reference current extraction of PI controller based on IRP theory

3.2. Control Algorithms

A DSTATCOM supplies reactive power as needed by the load for reactive power compensation, because only real Power is provided by the source, and load balancing is accomplished. Through balancing the source reference current. Reference source current used to determine when the DSTATCOM will switch on the load current actually has a fundamental frequency component whereby these approaches are used to extracted by ANFIS controller.

IRP theory is based on the calculation of instantaneous active and reactive power in the -frame and the transfer of three-phase quantities to two-phase quantities , . Figures 2 displays a block diagram of the reference extraction of using ANFIS controller with IRP theory. He controller receives sensed inputs VR, VY, and VB as well as iLR, iLY, and iLB. These quantities are then processed to create reference current commands (iSR, iSY, and iSB), which are then fed to a hysteresis-based pulse width modulated (PWM) signal generator (shown in Figure 2) to produce final switching signals fed to the DSTATCOM.

The system of the voltages is given by (1).

(1)

The load currents are shown in (2).

(2)

The R, Y, and B axes are fixed on the same plane and spaced out by 2/3 in R-Y-B coordinates. The "R" axis is the location of the instantaneous space vectors VR and iLR, whose amplitudes change with time in both positive and negative directions. This likewise applies to the other two phases. Using Clark's transformation, these phasors can be converted into-coordinates as shown in (3) and (4).

(4)

Where α and β are orthogonal coordinates. The power of conventional is defined as (5),

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)

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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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