Islanding Detection Inverter Matlab

J. Electromagnetic Analysis & Applications, 2009, 3: 170-180 Copyright © 2009 SciRes JEMAA

Distributed Generation

Alben CARDENAS, Kodjo AGBOSSOU, Mamadou Lamine DOUMBIA Institut de recherche sur l'hydrogène (IRH), Département de Génie Électrique et Génie Informatique, Université du Québec à Trois-Rivières, Trois-Rivières (Québec), Canada.

Received May 21st, 2009; revised July 23rd, 2009; accepted August 23rd, 2009.

Abstract

Islanding detection is an essential function for safety and reliability in grid-connected distributed generation (DG) sys- tems. Several methods for islanding detection are proposed, but most of them may fail under multi-source configura- tions, or they may produce important power quality degradation which gets worse with increasing DG penetration. This paper presents an active islanding detection algorithm for Voltage Source Inverter (VSI) based multi-source DG sys- tems. The proposed method is based on the Voltage Positive Feedback (VPF) theory to generate a limited active power perturbation. Theoretical analyses were performed and simulations by MATLAB /Simulink /SimPowerSystems were used to evaluate the algorithm’s performance and its advantages concerning the time response and the effects on power quality, which turned out to be negligible. The algorithm performance was tested under critical conditions: load with unity power factor, load with high quality factor, and load matching DER’s powers.

Keywords: Distributed Generation (DG), Interconnected Power Systems, Islanding Detection, Power Generation, Voltage Positive Feedback.

1. Introduction

The Distributed Energy Resources (DER) including Dis- tributed Generation (DG) and Distributed Storage (DS) are, as renewable energy resources, very important to improve power distribution reliability and capability.

Their penetration is increasing nowadays and their utili- zation shows potential for rural utility solutions . The Hydrogen Research Institute (HRI) has designed and developed a renewable energy (RE) system which in- cludes Photovoltaic (PV) arrays, Fuel Cells (FC) and Wind Turbine Generators (WTG) with an energy storage capability using electrolytic hydrogen . This RE sys- tem operates presently in stand-alone mode. It can be adapted for rural dispersed generation solutions and in- terconnected with the electric utility grid by using in- verter based interfaces (DC/AC static converter). Figure 1(a) is a simplified diagram of the basic RE unit as im- plemented at HRI. Figure 1(b) shows the possible multi- source DER system presently under construction.

An important technical issue with utility interfaced DER systems is unintentional islanding operation. The islanding condition occurs when the utility is discon- nected and the DG continues to supply power to the local load. This condition is not desirable because it can gener- ate voltage and frequency instability and power quality degradation; and it constitutes a great risk for mainte- nance personnel. In view of the importance of human and equipment protection, unintentional islanding for DG operation is not tolerated . For these reasons the detec- tion of unintentional islanding operation is required as rapidly as possible to allow the timely disconnection of , the DG disconnection is required within two seconds after the utility disconnection. Consequently, for safety DER integration, Anti-Islanding (AI) protection is a re- quirement.

Remote and local techniques are used for islanding de- tection. Remote techniques such as Supervisory Control and Data Acquisition (SCADA), Trip (disconnect) Signal and Power Line Carrier Communication (PLCC) systems are centralized methods implemented on the utility side.

They offer high performance and applicability on multi- source topologies. However, those centralized methods are expensive to implant . On the other hand, local techniques include passive and active methods which are

Implemented On The Dg Side. Local Passive Methods

have a large Non Detection Zone (NDZ), and hence are not useful for high DG penetration. A solution for the

Ndz Reduction Is The Utilisation Of Local Active

anti-islanding methods. Islanding Detection Method for Multi-Inverter Distributed Generation

171

Figure 1. Simplified diagram of stand-alone RE system implemented at HRI and the possible multi-source DER system Those active methods are currently based on the injec- tion of voltage, frequency or output power perturbations, and the subsequent monitoring for the detection of changes in electric parameters to confirm islanding con- dition. Those methods can detect the islanding condition, but one of their problems is that they can fail when mul- tiple sources are connected at PCC, because the effect produced by one source may be interfered by another one if synchronization between the multiple converters is not possible. Another drawback of active methods is that they can cause power quality disturbances as Total Voltage Harmonic Distortion (TVHD) increase and voltage and frequency fluctuations or instability. These problems become bigger if the introduced perturbation is increased to make possible the islanding detection , especially in systems with high penetration.

Use of the Correlation Function combined with active

Methods Is Proposed In And For Multi-Source

topologies. In , the correlation function is combined with an active method that introduces a constant alternat- ing perturbation of reactive power (±5% and ±10%), the anti-islanding algorithm is implanted in only one (master unit) of multiple DGs, and the others units use a passive anti-islanding scheme. The detection time depends on the output power of the master unit and on the reactive power perturbation level. In and , the correlation func- tion is combined with an active algorithm that introduces a user defined or random (M-sequence) perturbation of the output voltage (fixed to ±2V for 120V/60Hz system).

The correlation function may change with the number of connected DGs, and consequently a threshold adjustment is necessary if the number of units change.

In this article we propose an active islanding detection method based on Voltage Positive Feedback (VPF) and passive method Under/Over Voltage Protection and Un-

Der/Over Frequency Protection (U/Ovp-U/Ofp). The

proposed method can be used on multi-source configura- tions, and allows both unity power factor and power fac- tor improvement operation modes. This method intro- duces a limited active power perturbation proportional to measured variations of PCC voltage (VPCC). Simulations

Using Matlab™/Simulink™ And Simpowersystems

™ are carried out to validate the algorithm under several operating conditions.

2. Power Control Scheme

The system we consider is illustrated in Figure 1, where several DG units are interconnected with the utility at PCC. Each unit has an IGBT voltage source inverter (VSI) and its active and reactive power control using a current control scheme as shown by Figure 2.

In this power control scheme, the output current fun- damental magnitude (IINV(1)) and phase angle (I) are calculated respectively using (1) and (2).

* Are Respectively The Reactive And

active power external set points for the DG unit. The power angle I represents the phase angle between the inverter output fundamental current and the funda- mental voltage measured at PCC.

The resultant set-point current (3) is used to generate the switching signals for the IGBT bridge inverter, using Hysteresis Current Control (HCC) or Sinusoidal Pulse Width Modulation (SPWM) techniques.

(3)

where  and f are the phase angle and frequency of the voltage measured at PCC, and t is time in seconds. Considering that the proposed algorithm (see Section 3) Copyright © 2009 SciRes JEMAA Islanding Detection Method for Multi-Inverter Distributed Generation

172

Figure 2. Power control scheme for single grid connected DG unit Figure 3. Voltage positive feedback with d-q current control scheme introduces an active power perturbation that is added to the external set point, we are not limited to the Figure 2 power control scheme, and it may be changed to another one such as the d-q transformation based power control scheme . Notice that the d-q control scheme is con- venient when decoupled active and reactive power con- trol is required principally in three phase systems.

3. Islanding Detection Algorithm

This section describes the voltage positive feedback princi- ple and the proposed active islanding detection algorithm.

Detection Methods

Positive feedback with d-q current control based family of islanding detection methods is presented in and

. These Methods Consider The Relation Between The

active (P) and reactive (Q) powers with the voltage mag- nitude (V) and frequency (f) as shown in (4) and (5), and the effects of current magnitude and angle deviation on the output active and reactive powers.

(4)

Copyright © 2009 SciRes JEMAA Islanding Detection Method for Multi-Inverter Distributed Generation

(5)

where, =2f, and R, C and L are the resistance, capaci- tance and inductance of the resonant load. This family of islanding detection methods includes fre- quency and magnitude of voltage positive feedback based schemes. The positive feedback is used to generate a low frequency perturbation signal (∆id or ∆iq) that is added to

* And/Or Iq

* set points.

Figure 3 Shows The Principle Of Voltage Positive

Feedback (VPF) with d-q current control scheme. The d-axis component of VPCC (Vd) is monitored and filtered using a band pass filter (BPF) to obtain the voltage varia- tion ∆Vd, this voltage variation is amplified with a preset gain G (A/V) and used as d-axis current perturbation (∆id). The d-axis current perturbation signal affects di- rectly the inverter output power and consequently the VPCC magnitude and frequency in islanded mode. A satu- ration block is used to limit the output current perturba- tion. As a result, on islanding condition a rising deviation of frequency (df) or magnitude (dV) of VPCC is observed,

And This Deviation Can Trip U/Ovp Or U/Ofp For Dg

safety disconnection. An important characteristic of the VPF based methods is the low power quality degradation in contrast with other active methods that use distorted signals injection, as proposed in and .

On the other hand, the time necessary to generate the trip signal using the VPF based method is determined by the load quality factor qF (6) and the feedback preset gain G. One simple way to improve the response speed is to increase G, but this solution increases the risk of voltage or frequency instability, especially in multi-source to- pologies.



Figure 4. Proposed voltage positive feedback scheme voltage measured at PCC (VRMS) as the feedback variable to generate a limited active power perturbation. The ba- sics of the proposed scheme are presented in Figure 4.

The VRMS (after the LPF filter) is compared with a refer- ence voltage VREF, and the difference ∆V is used to cal- culate the active power perturbation ∆P.

The reference voltage VREF[k+1] is set initially equal to the nominal RMS voltage (VNOM), and is subsequently updated only on System Stable Condition (SSC) using the historic RMS average voltage VAV (7). Otherwise, the new voltage reference (VREF[k+1]) is set equal to the old reference value (VREF[k]) according to (8). The SSC is defined as the condition where both the power and the voltage perturbations (∆P and ∆V) are stable.

The Sandia Voltage Shift (Svs) Method Uses The

utility voltage to calculate the output current amplitude; in this method the average voltage of the utility is com- pared with the actual voltage in each electric cycle (or- half cycle) to calculate the current perturbation that is amplified by a preset gain.

In both methods, SVS as well as VPF with d-q transforma- tion, the output voltage at the islanding condition is forced to the trip points of the U/OV protection by an important output current reduction or increase, and it is finally the U/OVP that shuts down the power converter. This important perturbation of the output current before the disconnection may affect the load, and is not appropriate if stand-alone operation of the system is desired after the safety disconnection.

(7)

where, m is the number of samples considered for the average calculation.

(8)

The active power perturbation ∆P is calculated using

The Maximal Allowed Power Perturbation ∆Pmax, The

minimal power perturbation ∆PMIN, a gain factor G, and the difference between VREF and VRMS, according to (9,10) and (11).

3.3 Parameter Selection

The parameters ∆PMIN, ∆PMAX and G are selected to pro- duce a low active power variation in the interconnected mode and a low voltage variation in the islanding detec-

Nnected Mode, The Active Power Deviation Is

reduced to minimal ∆PMIN on voltage stability condition. The expected evolution of voltages (VREF and VRMS) and

The Active Power Perturbation For The Islande

n period. Considering an ideal utility source, the volt- age error in the interconnected operation mode may be close to zero, but in practice a minimal error is always resent, and this error affects the real output power. We set the ∆PMIN near the mean active power perturbation cal- culated using the typical utility voltage variations (εMIN).

Based on the measured voltage of the utility, we take a εMIN=0.167% (0.2V at 120V) as the minimal voltage error. This εMIN allows us to set a minimal active power perturbation ∆PMIN=0.5% using a gain of G=3. At the islanding condition, if the system operates at unity power factor, this ∆PMIN introduces a voltage variation of

0.25% (0.3V At 120

To limit the effects on the output voltage in the detec- tion period, we set the ∆PMAX=2.5% to produce a maxi- mal voltage error ε= 1.24% (1.5V at 120V). This set- ting permits the islanding detection without output volt- age degradation if the load and DG powers are close or matched.

D Mode Is

Figure 5. Expected effect of the proposed VPF scheme un-

Der Islanding Condition

Figure 6. Expected effect of the proposed VPF scheme un- der voltage reduction and normal voltage variation shown by Figure 5. Before the utility disconnection (t

Produces A

negative increasing of ∆P until its saturation at t=t1. The

Consequently, The Vref Is Updated And Th

oltage variation.

3.5 Islanding Confirmation

If the average of the magnitude of the active power per-

Counter) And ∆Prc (Reset Counter) To Activate Or

a time counter (TC). If the TC count is larger than a p limit of time TMAX, the islanding condition can be confirmed.

Rms

tage. After the utility disconnection (t>t0), if VRM

L

VREF, the negative ∆PMIN and the VPF effect produce a progressive VRMS reduction and negative increase of ∆P, until the ∆P saturation at t=t1.

The SSC condition is reached at t=t2 with voltage sta- bilization, and VREF is updated to VAV. Consequently, a reduction and a subsequently positive increase of ∆P are expected to produce a voltage level increase (from t2 to t4). The power perturbation is saturated at t=t3, and a new SSC is reached at t=t4. A cyclic power perturbation and a voltage level oscillation can be observed and used to con- firm the islanding condition.

The expected trajectories for a voltage variation are presented in Figure 6. In this case, if an important voltage

Variation Is Occurred At T=T0, The Vpf Effect

ilization at t=t2.

E Active Power

perturbation is reduced to ∆PMIN. Subsequently the minimal active power perturbation may be observed as an

Effect Of The Normal V

rbation (∆PAV) is calculated and observed during a de- tection period TDET, then under islanding condition the expected profile of this variable is shown in Figure 7, and we can use this new variable to establish the islanding condition. For islanding condition confirmation, the ∆P

Reset

Copyright © 2009 SciRes JEMAA

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

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