2010 12th International

Conference on Optimization of

Electrical and Electronic

Equipment

(OPTIM 2010)

Brasov, Romania20-22 May 2010

Pages 656-1310

4 IEEE IEEE Catalog Number: CFPl 022D-PRT

ISBN: 978-1-4244-7019-8

2/2

VOLUME 2

CASCADE BASED CONTROL OF A DRIVETRAIN WITH BACKLASH 656Florin Caruntu Conslantin, Elena Balau Andreea, Corneiin Lazar

ACTIVE DYNAMIC DAMPING OF TORSIONAL VIBRATIONS BY Hoo-CONTROL 662

C. Sowkounis

NONCOHERENT DEMODULATION OF CONTINUOUS PHASE MODULATED SIGNALSUSING EXTENDED KALMAN FILTERING 670

Janos Gal, Andrei Campeamt, loan Nafornita

DEVELOPMENT OF HARDWARE REDUNDANT EMBRYONIC STRUCTURE FOR HIGH

RELIABILITY CONTROL APPLICATIONS 674

Cs. Szasz, V. Chindris

ON THE DEVELOPMENT OF AN EXPERIMENTAL CAR-LIKE MOBILE ROBOT 680

Robert Gall, Fritz Trosler, Gheorghe Mogan

MECHANICAL-ELECTRICAL OPTOISOLATOR TRANSDUCERS 686

loan Marcel Citirus, Mihai Dimian, Adrian Gruur

INTELLIGENT ACTIVE VISION SYSTEM FOR AUTONOMOUS ROBOTS 692F. Moldoveanu, C. Boldisor, D. Floroian, C. Suliman, C. Suciu

DYNAMIC LOAD CHANGE STRESS MINIMIZING CONTROL OF ELASTICALLY COUPLEDMULTI-MASS SYSTEMS 700

Matthias Joost, Christian Mehler, Bernd Orlik

DESIGN, IMPLEMENTATION AND MONITORING OF A SCREW ORDER HANDLING

PROCESS USING BUSINESS PROCESS MANAGEMENT TOOLS 706

Alina Girhea, Francisc Sisak, Liviu Pernin

EFFECTIVE OPTIMIZATION OF CONTROLLERS STABILIZING CLOSED CIRCUIT

GRINDING SYSTEMS OF CEMENT 714

D. Tsumalsoulis, C. Lungoci

OPTIMAL FPGA IMPLEMENTATION OF GARBF SYSTEMS 720

/. C. Vizitiu, I. C. Rincu, A. Radtt, I. Nicolaescu, F. Popescn

BUFFERING APPLICATION FOR AN INDUSTRIAL MONITORING SOFTWARE SYSTEM 726

Costin-Marhis Grigorescu, Sorin-Aurel Moraru, Florian Neukart, Milian Badea

DATA ACQUSITION, STORAGE AND ONLINE RETRIEVAL OF CHROMATOGRAPHY FROMAUTOMATED HPLC FOR PHARMACEUTICAL RESEARCH APPLICATIONS 732

Mohammad Shukri Hapeez, AhmadIhsan Mohd Yassin, Habihah llashim, Mtistafar Kamal Hamzah

OPTIMAL FPGA IMPLEMENTATION OF GAMLP SYSTEMS 741/. C. Vizitiu, I. C. Rincu, F. Popescu

VISUAL CONTROL ARCHITECTURE OF SERVOING SYSTEMS BASED ON IMAGE

MOMENTS 747Cosmin Copot, Adrian Burlacti, Cornelia hazar

THE SYNCHRONOUS CONTROL OF NODES AND THE MESSAGE TIME DELAYS

ESTIMATION FOR A CAN NETWORK 753

D. Puiu, F. Moldoveanu, S. Ryvkin

MODELING A KNOWLEDGE BASE FOR GENERATING NEW TRAJECTORIES FOR A

ROBOT ARM LOCATED IN A CELL OF A FLEXIBLE FABRICATION LINE 759

G. A. Calcmghi, I. Sarkcmy, M. Stoica, F. Sisak

SURVEILLANCE MULTIAGENT SYSTEM USING ROBOT VISION BASED ON FUZZY

CONTROLLER 765

D. Floroian, F. Moldoveanu, S. Ryvkin, C. Sitciu

WLAN ROAMING SIMULATOR 771

loana Cobeami, C. Catrinescu, loan Margineami, Lucian Mihai llu

A METHOD PROPOSED FOR TRAINING AN ARTIFICIAL NEURAL NETWORK USED FOR

INDUSTRIAL ROBOT PROGRAMMING BY DEMONSTRATION 777

M. Stoica, G A. Calangiu, F. Sisak, 1. Sarkany

A COMPARATIVE ANALYSIS OF TWO SELF-LEARNING BASED STRATEGIES FOR FUZZY

CONTROLLER DESIGN 783

C. Boldisor, V. Comnac, I, Topa, S. Coman

A FUZZY APPROACH REGARDING THE ANALYSIS OF WINE PRODUCTION DEPENDING

ON DIFFERENT ENVIRONMENTAL PARAMETERS BASED ON STATISTICAL SIGNAL

PROCESSING 789

A lexandru-Mihnea Spiridonica, Julian Sloleriu, Romeo Cristian Ciobami

EXTENDED RRT ALGORITHM WITH DYNAMIC N-DIMENSIONAL CUBOID DOMAINS 797

Chrislas Fragkoponlos, Axel Graeser

FRACTIONAL ORDER CONTROL FOR DC ELECTRICAL DRIVES IN NETWORKED

CONTROL SYSTEMS 804

Simona Coman, V. Comnac, Cr. Boldisor, D. C. Dumitrache

BOILER-TURBINE SIMULATOR WITH REAL-TIME CAPABILITY FOR DISPATCHER

TRAINING USING LABVIEW 810

Mihai Jacob, Gheorghe-Daniel Andreescu, Nicolai Muntean

A COMPOSABLE, ENERGY-MANAGED, REAL-TIME MPSOC PLATFORM 816

Anca Molnos, Jude Angelo Ambrose, Kees Goossens

MODEL DRIVEN DEVELOPED MACHINE VISION SYSTEM FOR SERVICE ROBOTICS 823

S. M. Grigoresai, 0. Premel, A. Graser

ARTIFICIAL OLFACTION SYSTEM WITH HARDWARE ON-CHIP LEARNING NEURAL

NETWORKS 830

Aim Tisan, Marcian Cirstea, Stefan Oniga, Attila Buchman

ELECTROCARDIOGRAM BASELINE WANDER REMOVAL USING STATIONARY WAVELET

APPROXIMATIONS 836

Beatrice Arvinly, Dnmitru Toader, Marius Costache, Alexandra Isar

FOCUSING CONTROL UNDER MICROSCOPIC VIEWS BASED ON REGIONAL MONOTONES

OF IMAGE FOCAL VALUES 842

Kit Chin Lin

DATA ACQUISITION AND VIRTUAL INSTRUMENTATION SYSTEM FOR THE STUDY OF

PELTIER AND SEEBECK EFFECTS 849

Florin Sandu, Venetia Sandu, Stefan Nan, Adrian Julian Dnmitru

SELF-TUNING CURING OVEN CONTROL 855

R. M. Schoeman, J. F. J. Van Rensburg, D. V. Nicolae

A CALIBRATION LABORATORY FOR ROGOWSKI COIL USED IN ENERGY SYSTEMS AND

POWER ELECTRONICS 859

A. Murinescu

HARDWARE IMPLEMENTATION OF A REAL-TIME 3D VIDEO ACQUISITION SYSTEM 866

Jstvan Andorko, Peter Corcoran, Petronel Bigioi

REAL NETWORK TRAFFIC ANOMALY DETECTION BASED ON ANALYTICAL DISCRETE

WAVELET TRANSFORM 872Marius Salagean

COMPARISON OF WAVELET FAMILIES WITH APPLICATION TO WIMAX TRAFFIC

FORECASTING 878Cristina Slolojescu, Ion Railean, Sarin Moga, Alexandru Isar

PAPR REDUCTION IN MULTICARRIER MODULATIONS USING GENETIC ALGORITHMS 884

Marco Lixia, Maurizio Murroni, VladPopescu

MODEL OF SUPERCAPACITOR-STARTER ASSEMBLY USED FOR INTERNAL

COMBUSTION ENGINES STARTING 889

Aurel Cornel Stanca, PaulNicolae Borza, Mihai Romanca, Roxana Paun, Sarin Zamfir

SINGLE-STAGE TRAVELLING WAVE AMPLIFIER FOR POWER APPLICATIONS 895

Matin Yazgi, Mustafa Sayginer, Bal S. Virdee, AH Taker, Hakan Kuntman

COMPARING THE PERFORMANCE OF DUO-BINARY TURBO CODES ON RAYLEIGH

CHANNEL 899

Maria Kovaci, Horia Balta

CAD OF MIMO SYSTEMS BY REDUCED ORDER MODELING 903

Lucia Dumitriu, Mihai Iordache, Luciati Mandache

GALS-SA: SYNCHRONOUS-ASYNCHRONOUS CONFIGURABLE PLATFORM 909

Razvan Jipa, Traian Tulbure

A NEW WIDEBAND FULLY-DIFFERENTIAL LNA WITH MATCHED INPUT AND OUTPUT

IMPEDANCES 917

Bahvant Godara, YahyaLakys, Alain Fabre

REDUCING NOISE IN EMBEDDED SYSTEMS BY CLOCK SIGNAL SPECTRUM SPREADING 921

Pelre Ogrutan, Csaba-Zollan Kertesz, Gheorghe Pana

INTELLIGENT IMAGE DATA PROCESSING FOR ACQUIRING TARGET IN A

MULTISENSOR PLATFORM 927

Octavian Grigore-Muler, Mihai Barbelian, Janel Arhip, MihaiJurba

IMPROVED CONTOURS FOR TOF CAMERAS BASED ON VICINITY LOGIC OPERATIONS 935

Gabriel Danciu, Mihai Ivanovici, Vasile Buzuloiu

ASPECTS ON THE STABILITY OF A GYROSCOPIC ANGLE MEASUREMENT SYSTEM 939

Carmen Gergan, Petre Ogrutan, Lash Nagy

TESTBENCH COMPONENTS VERIFICATION USING FAULT INJECTION TECHNIQUES 943

N. A. Banciu, G. Toacse

ADAPTIVE HARDWARE-SOFTWARE CO-DESIGN PLATFORM FOR FAST PROTOTYPING

OF EMBEDDED SYSTEMS 950

Stefan Oniga, A/in Tiscin, Claudiu Lung, Attila Buchman, loan Orha

OPTIMIZATION OF SIGMA-DELTA MODULATOR BASED ON ARTIFICIAL IMMUNE

ALGORITHMS 956

Gabriel V. lana, Gheorghe Serban, Petre Anghelescu, Lattrentiu lonescu

MEASUREMENT SYSTEM FOR NON-DESTRUCTIVE TESTING USING ULTRASONIC

TOMOGRAPHY SPECTRAL ATTENUATION 962

Giovaima Concit, Barbara De Nicola, Carlo Piga, Vlad Popescti

A DESIGN APPROACH TO CHARGE INJECTION MODELING 967

Laszlo Szilagyii, Andrei Danchiv, Mircea Bodea

A GENERALIZED APPROACH TO COMPLEX FILTER DESIGN WITH OPEN LOOP ACTIVE

CELLS 973

Gabor Csipkes, Doris Csipkes, Hernando Fernandez-Canque, Sarin Hinlea

VOICE QUALITY DEGRADATION RECOGNITION USING THE CALL LENGTHS 980

Zoltan Caspar, Izabella Gocza

MIDDLEWARE FOR WIRELESS SENSOR NETWORKS BASED ON SERVICE DELIVERY

FRAMEWORK 986

Virgil Cazacu, Laura Cobarzan, lulht Szekely, Vasile Dadarlat, Florin Sandu

ETHERNET COMMUNICATION FOR DETECTION OF EMERGENCY LOCATIONS AND

DYNAMIC EVACUATION IN UNDERGROUND INFRASTRUCTURES 992

Chrisloph Muller, Andreas Noack

THE QUALITY OF MULTIPLE VOIP CALLS IN AN ENCRYPTED WIRELESS NETWORK 998

Mihai Ivanovici, Stefan Savu

METHODS FOR INCREASING THE ACCESS TO INFORMATION DATABASES USING OPEN

SOURCE TOOLS 1002

Catalin loan Maican

EMBEDDED SYSTEMS PLATFORM-BASED DESIGN FROM TEACHING TO INDUSTRY OR

VICE-VERSA 1008

Nicusor Birsan, Horia Cornel Hedesht

A VIRTUAL REALITY BASED HUMAN-NETWORK INTERACTION SYSTEM FOR 3D

INTERNET APPLICATIONS 1016

Vlad Cristian Sloianovici, Don Talaba, Adrian-Valentin Nedelcu, Mihai Machedon Pisu, Florin Barbuceami,

Adrian Stavar

INSTRUCTIONAL DESIGN IN THE VOCATIONAL TRAINING ON "COMPUTER

NETWORKING" 1024

Venetia Sandu, Adrian-Valentin Nedelcu, Sorin Alexandru Cocorada, Sebastian Dim

HIGH-PERFORMANCE MAGNETIC GEARS TOPOLOGIES 1031

Oviditi S. Chirila, Dan Stoai, Mihai Cental, Kay Hamayer

VARIABLE STEP SIZE P&O MPPT ALGORITHM FOR PV SYSTEMS 1037

AhmadAI-Diab, Conslantinos Sowkounis

AN FPGA CONTROLLER FOR A COMBINED SOLAR / WIND POWER SYSTEM 1043

Martian Cirstea, Alberto Parera-Ruiz

NEW LOW COST STRUCTURE FOR DUAL AXIS MOUNT SOLAR TRACKING SYSTEM

USING ADAPTIVE SOLAR SENSOR 1049

Alin Argeseami, Eiven Ritchie, Krisztina Leban

CURRENT-VOLTAGE CHARACTERISTIC RAISING TECHNIQUES FOR SOLAR CELLS.

COMPARISONS AND APPLICATIONS 1055

Daniel T. Cotfas, Petru A. Cotfas, Doru Vrsutiu, Cornel Samoila

POWER INJECTION SYSTEM FOR PHOTOVOLTAIC PLANTS BASED ON A

MULTICONVERTER TOPOLOGY WITH DC-LINK CAPACITOR VOLTAGE BALANCING 1061

Victor Minambres-Marcos, Enrique Roinero-Cadaval, Maria Isabel Milanes-Montero, Migues Angel Guerrero-

Martinez, Ferritin Berrero-Gonzalez, Pedro Gonzalez Caslrillo

SINGLE-PHASE GRID-CONNECTED DISTRIBUTED GENERATION SYSTEM WITH

MAXIMUM POWER TRACKING 1071

R. Bojoi, D. Roil/, G. Griva, A. Tenconi

ELECTRICAL RESPONSE OF AN OPTIMIZED ORIENTED PHOTOVOLTAIC SYSTEM 1078

Bogdan Burduhos, Darin Diaconescu, Ion Visa, Anca Duta

PHOTOVOLTAIC EFFICIENCY OF A GRID CONNECTED 10 KWP SYSTEM IMPLEMENTED

IN THE BRASOV AREA 1086

Alexandru Enesca, Mihui Comsit, Ion Visa, Anca Duta

OVERVIEW OF RECENT GRID CODES FOR WIND POWER INTEGRATION 1092

Mufit Altin, Omer Goksu, Remus Teodorescu, Pedro Rodriguez, Birgittc-Bak Jensen, Lars Helle

METHODS FOR COGGING TORQUE REDUCTION OF DIRECTLY DRIVEN PM WIND

GENERATORS 1101

Tiberiu Tudorache, Leonard Melcescu, Mihail Popescu

A NEW CONVERSION AND CONTROL SYSTEM FOR A SMALL OFF - GRID WIND TURBINE 1107

Nicolae Muntean, Octavian Cornea, Diana Petrila

POWER LOSSES ANALYSIS OF TWO-LEVEL AND THREE-LEVEL NEUTRAL CLAMPED

INVERTERS FOR A WIND PUMP STORAGE SYSTEM 1114

L, Clolea, A. Forcos, C. Marinescu, M. GeorgescuSTORAGE ANALYSIS FOR STAND-ALONE WIND ENERGY APPLICATIONS 1120

Luminita Barote, Corneliu Marinescu

A LOOK AT THE ROLE AND MAIN TOPOLOGIES OF BATTERY ENERGY STORAGE

SYSTEMS FOR INTEGRATION IN AUTONOMOUS MICROGRIDS 1126/. Serban, C. Marinescu

SMART ELECTRICAL ENERGY STORAGE SYSTEM FOR SMALL POWER WIND TURBINES 1132M. Georgescu, L. Barote, C. Marinescu, L. Clolea

STUDY OF A GRID-CONNECTED HYBRID WIND/MICROHYDRO POWER SYSTEM

ASSOCIATED WITH A SUPERCAPACITOR ENERGY STORAGE DEVICE 1138

Stefan Breban, Benoil Robyns, Mircea M. Radulescu

CONTROL OF PARALLEL OPERATING MICRO HYDRO POWER PLANTS 1144

C. P. Ion, C. Marinescu

LOW-COST, HIGH FLEXIBILITY I-V CURVE TRACER FOR PHOTOVOLTAIC MODULES 1150

Men Joseha Maestro Ibirriaga, Xabier Miquelez De Mendiluce Pena, Adrian Oprilescu, Dezso Sera, Remus

Teodorescu

DYNAMIC MODEL OF A SMALL HYDROPOWER PLANT 1156C. Jalin, I. Visa, D. Diaconescu, R. Saitlescu, M. Neagoe, O. Climescu

OPTIMAL SELECTION OF WIND TURBINE FOR A SPECIFIC AREA 1164

Ciprian Nemes, Florin Munteanu

MONITORING OF A GROUND SOURCE HEAT PUMP WITH HORIZONTAL COLLECTORS 1170

Adrian Virgil Craciun, Florin Sandu, Gheorghe Pana

CHALLENGES FOR SMART DISTRIBUTION SYSTEMS: DATA REPRESENTATION AND

OPTIMIZATION OBJECTIVES 1176

Gianfranco Chicco

FROM MICROGRIDS TO SMART GRIDS: MODELING AND SIMULATING USING GRAPHS.

PART I ACTIVE POWER FLOW 1185

C. Marinescu, A. Deacomi, E. Ciurea, Daniela Marinescu

FROM MICROGRIDS TO SMART GRIDS: MODELING AND SIMULATING USING GRAPHS.

PART II OPTIMIZATION OF REACTIVE POWER FLOW 1191

C. Marinescu, A. Deaconu, E. Ciurea, Daniela Marinescu

A STUDY ON WIND ENERGY GENERATION FORECASTING USING CONNECTIONIST

MODELS 1197

hilia Coroama, Mihai Gavrilus

INTELLIGENT DISTRIBUTED STATE ESTIMATION USING REI SYSTEM EQUIVALENTSAND PHASOR MEASUREMENTS 1203

Ovidiu Ivanov, Mihai Gavrilas, Gh. Asachi

LOAD CURVE ANALYSIS FOR AN INDUSTRIAL CONSUMER 1209

Catalin Mihai, Ionel Lepadat, Elena Helerea, Daniel Culin

EVALUATION OF THE PERFORMANCES OF EFFICIENT TRANSFORMERS IN

DISTRIBUTION NETWORKS BY FUZZY TECHNIQUES 1215

Gheorghe Grigoras, Gheorghe Carlina, Elena-Crenguta Bohric, Fiorina Rotaru

SELECTIVE DETECTION OF SIMPLE GROUNDING FAULTS IN MEDIUM VOLTAGE

POWER NETWORKS WITH RESONANT EARTHED NEUTRAL SYSTEM 1219

Dumitru Toader, Pelru Ruset, Stefan Haragus, Constant'm Blaj, loan Hategan, Nicolae Pinte, hilia Cata

DETERMINATION OF TYPICAL LOAD PROFILES IN HYDRO-POWER PLANT BY

CLUSTERING TECHNIQUES 1228

Daniela Comanescu, Gheorghe Grigoras, Gheorghe Cartina, Fiorina Rotaru

OZONE SYNTHESIS UNDER SURFACE DISCHARGES GENERATED ON THE ALUMINA

CONCENTRIC ACTUATOR 1232

S. Jodzis, T. Smolinski, P. Sowka

DESIGN OF A PLASMA GENERATOR BASED ON E POWER AMPLIFIER AND IMPEDANCE

MATCHING 1237

Dorin Petrous, Alin Grama, Sergia Cadar, Emit Plaian, Adina Rush

PULSED DIELECTRIC BARRIER DISCHARGE GENERATED AT THE GAS-LIQUIDINTERFACE FOR THE DEGRADATION OF THE ORGANIC DYE METHYL RED IN

AQUEOUS SOLUTION 1243

D, Piroi, M. Magureanu, N. B. Mandache, V. David, V. Parvulescu

REACTIONS INDUCED BY ELECTRICAL DISCHARGES IN POLLUTANT ABATEMENT AND

BACTERIAL INACTIVATION 1249

D. Moussa, M. Naitali, J. M. Henry, B. Hnatiitc, J. L. Brisset

DIRECT AND POST-DISCHARGES IN ENVIRONMENTAL APPLICATIONS OF COLD

PLASMAS 1256

G. Kamgang Youhi, M. Naitali, J. M. Herry, E. Hnatiuc, J. L. Brisset

REDUCTION OF ELECTROMAGNETIC PERTURBATIONS FOR COLD PLASMA

ELECTROCHEMICAL REACTORS USING ELECTROMAGNETIC SCREENING 1263

G, Todirasi, E. Hnatiuc, R. Burlica, B. Hnatiuc, B. Gavril

THE IGNITION AND CONTROL CONDITION FOR THE USEFUL DISCHARGE IN A

GLIDARC REACTOR WITH PLANE GEOMETRY AND AUXILIARY ELECTRODES 1269

E. Hnatiuc, J. L. Brisset, B. Hnatiuc

HYDROGEN AND HYDROGEN PEROXIDE FORMATION IN THE AC WATER-SPRAY

GLIDING ARC REACTOR 1275

R. Burlica, B. Hnatiuc, E. Hnatiuc

THE STUDY OF AN ELECTRIC SPARK FOR IGNITING A FUEL MIXTURE 1281

B. Hnatiuc, S, Pellerin, E. Hnatiuc, R. Burlica

FUTURE ELECTRONIC POWER DISTRIBUTIONSYSTEMS - A CONTEMPLATIVE VIEW - 1287

Dushan Boroyevich, Igor Cvetkovic, Dang Dung, Rolando Burgos, Fei Wang, FredLee

HIGH-EFFICIENCY VARIABLE-SPEED ELECTRIC MOTOR DRIVE TECHNOLOGIES FOR

ENERGY SAVINGS IN THE US RESIDENTIAL SECTOR 1299Dan M. Ionel

Author Index

Optimal Selection of Wind Turbine For a Specific Area

Ciprian Nemes, Florin Munteanu “Gheorghe Asachi” Technical University Iasi

cnemes@ee.tuiasi.ro Abstract - In this paper a method for reliability estimation of power systems including wind energy generators is presented. The method is useful to compare different wind turbine generator types and to establish the suitable wind generator type in a given area. The wind generator parameters to be considered for an optimal selection are also presented in details, having in view the improvement of system reliability indices concerning mainly the available generated power.

I. INTRODUCTION

Power generation planning including the sources structure is a classic and complex activity in power systems. The basic purpose is to determine the best solutions for the generation sources to meet load growth, with acceptable level of reliability. Generally, the planning process does not consider the influence of transmission system, but only concentrates on the balance between generation and load systems. The procedure for the expansion planning of generating capacity by adding new units is based on an acceptable level of reliability and having in view the rate of expected load growth.

During the last time, a growing interest in renewable energy resources has been observed. Wind is one of fastest growing energy source and is considered as an important alternative to conventional power generating sources.

In this paper, a reliability planning method for a generating system with a wind energy system (WES) is presented. Even the major benefits due to the presence of WES in power networks, there are also some challenges related to wind generation capacity unpredictability, intermittence and high fluctuation, due to strongly dependence of the natural resource availability.

Supplementary, the electricity production by a WES in a given area is depending on many factors. These factors include the wind speed conditions in the area, and most importantly, the characteristics of the wind turbine generator (WTG) itself, particularly the cut-in, rated and cut-out wind speed parameters. The power output of a WTG does not vary linearly with the wind speed. A wind turbine is not operational when the wind speed is below the cut-in speed vcut-in and will be stopped for safety reasons if the wind speed is higher than the cut-out speed vcut-out. In both extreme cases, the power output is zero. The power output of a WTG increases with the wind speed between the cut-in speed and the rated wind speed vrated, after that the power output remains constant at the rated power Prated [1,2].

Different types of wind turbines are commercially available on the market. Wind turbines range from less than 1kW to as large as 5 MW or more. It is therefore desirable to select a

wind turbine which is best suited for a particular area in order to obtain the maximum generation capacity benefit with respect of the given reliability level criterion.

Selection of the optimal WTG was discussed in different manner in various papers. Different authors focus their research to select the best suitable WTG that maximize the capacity factor [3]. The capacity factor is defined as the ratio of the expected output power to the rated power of WTG. Capacity factor for several WTG were computed having in view the Weibull distribution of the wind speed. This method gives an idea of a characteristic of the WTG based on wind speed associated to a given area, but completely ignores the load profile of the system where it must be added. Other method [6,7,8] is based on reliability indices evaluation for the generated power planning.

Having in view these issues, in the paper the reliability planning method is applied to have a comparison between different WTG types included in WES while maintaining the reliability level and establish the suitable WTG type for a specific area. Individual turbines are interconnected between them and connected to electric power network. The wind park effect refers to the loss of wind energy due to mutual interference between turbines is not consider in the paper.

II. POWER SYSTEM RELIABILITY ASSESSMENT

Reliability in its wide sense refers to the probability that a component or system comprising components is able to perform its intended function satisfactorily during a specified period of time under normal operating conditions. Thus, the reliability associated with a power system, in a general sense, is a measure of the overall ability of the system to generate and supply electrical energy. Power system reliability can be further divided into the two distinct categories of system adequacy and system security [4,5]. The basic facilities of a system are divided in three main sectors: generations, transmission and distribution, which are usually vertically integrated.

The adequacy associated to a generating power system is a measure of the ability of system generating capacity to satisfy the total system load. The usual reliability indices used in generating capacity evaluation are Loss of Load Probability (LOLP, %), Loss of Load Expectation (LOLE, h/year), Loss of Energy Expectation (LOEE, MWh/year), Loss of Load Frequency (LOLF, occurrence/year), Energy Index Reliability (EIR, %), and other [4-8].

Previous reliability indices give an idea of the possibility of generating system malfunction, but the information about the effects of possible increases the load on the system, is missing.

1224978-1-4244-7020-4/10/$26.00 '2010 IEEE

2010, 12th International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2010

A WES has a different impact on the load carrying capability of a generating system than does a conventional energy system. This is due to the variation in wind speed, the dependencies associated with the power output of each WTG in a wind farm, and the nonlinear relationship between WTG power output and wind speed.

For these reasons, in the power systems with WES, two new capacity benefit indicators were introduced to describe the reliability systems [8,9,10], namely: Incremental Peak Load Carrying Capacity (IPLCC, MW)

The IPLCC index evaluates the possible increasing of peak load system, maintaining the original adequacy level, for every power unit of WES added in system.

WTwithoutWTwith PLCCPLCCIPLCC −= (1)

PLCCwithout WT is the peak load that the generating system can carry for an adequacy level. PLCCwith WT is the peak load that the expanded generating system (with the additional of WTG’s) can carry at the same adequacy level.

Load Carrying Capability Benefit Ratio (LCCBR, %) LCCBR is defined as the ratio of the incremental peak load carrying capacity (IPLCC) due to addition of generating capacity to the amount of capacity added from WES. The LCCBR evaluates how much peak the load can be increased, due to the one unit power of WES addition in system, maintaining the original reliability level.

WTratPIPLCCLCCBR = (2)

Equivalent Capacity Ratio (ECR, %) This indicator evaluates the equivalence between conventional generation capacity and a WTG capacity. A 1 MW WTG cannot usually carry the same amount of load as a 1 MW from conventional generators (CG). If the additional wind capacity is replaced by conventional units with the same capacity PratCG, (MW), the corresponding incremental peak load carrying capability can be designated as IPLCCCG. The Equivalent Capacity Ratio (ECR) is defined as the ratio of the incremental peak load carrying capability of a WES addition and the incremental peak load carrying capability of a conventional generation addition.

CG

WT

IPLCCIPLCC

ECR = (3)

These three indices are used to indicate capacity benefit for a wind energy system addition. The capacity benefit indices IPLCC, LCCBR and ECR give a more direct and significant indication of the benefits of WES additions compared to the generation adequacy indices, LOLP, LOLE, LOEE, etc.

III. EVALUATION METHODOLOGY

The basic approach to evaluate the reliability of an electrical power generating system consists of two parts: capacity model and load model [4-8]. Reliability indices evaluation may be performed considering deterministic and

probabilistic approaches. Probabilistic methods can provide more significant information for system planning, since they consider probabilistic aspects of generating units. In its simplest form, the capacity model and the load model are convolved to create a probabilistic model which defines the reliability performance of the generating system in terms of adequacy and capacity benefit indices [11].

For a system, containing conventional generators and WTGs, the method proposed in this paper combines conventional generation and wind generation into separate groups. One group contains the conventional generators and the other group contains WTGs. For each of these groups a generation system model is developed using a convolution method, considering full capacities of all units and their forced outage rates (FOR). By combining the developed models of the conventional and wind generations, the probabilistic model of generation system can be obtained. After that, the generation and load models are combined to find the adequacy and capacity benefit indices, as is shown in figure 1.

Reliability indices

Capacity

fC(C)

Load

fL(L)

Conventional Capacity

fCG(CG)

WECS Capacity

fWTG(WTG)

Fig. 1. The reliability indices evaluation

Thus, for the power system, composed from conventional generators and wind turbine generators, designed to supply the load, the adequacy indices can be obtained by convolving all the probability functions of following variables. A. Probabilistic load model

The load demand in power systems is variable in time. There is no one unique profile or mathematical equation that can be adopted to represent the load characteristic curve. So, the load can be assumed to be a random variable with a specific probability function. The load probability function can be constructed from the peak load characteristic curve for each hour of a year. Usually, it is assembled using hourly peaks in ascending order, and in correspondence is indicate the percentage of times when the peak exceeds the amount of load.

To evaluate the adequacy indices and capacity benefit, in the presented convolution model, the load probability function has been modelled using the load model of IEEE-RTS. In the load model IEEE-RTS, the data are given as

1225

percentages of the annual, weekly and daily peak of load. With the knowledge of the annual peak demand, the hourly chronological load profile and the load probability function can be established. B. Probabilistic model of Conventional Capacity

The capacities of conventional generation units can be modelled like a discrete random variable. Is assumed that any unit has two possible working states, up or down, the probability of the down state being given by FOR. The conventional capacity probability function may be computed using the gradual convolution for all units of system. The probability function is associated with a table that contains all the capacity states, in an ascending order, and its probability. C. Probabilistic model of WES

Many studies have reported statistical tests on wind speed using different distributions [12,13]. The conclusion of these studies is that the Weibull distribution of two parameters may be successfully utilized to describe the principle wind speed variation. The wind speed is treated as a random variable assumed to have a Weibull distribution with a scale parameter α (m/s) and a shape parameter β (dimensionless). The estimates of the parameters of the Weibull distribution can be found using different estimation methods [14]. Each method has a criterion, which yields estimates that are best in some situations.

The power produced by a wind turbine generator is a function of the wind speed. A wind farm is a group of N wind turbine generators in the same location used for production of electric power, each WTG extracting some of the energy of the wind. For whole WTGs, simultaneously running, the output power from wind farm is given by the sum of the output power of each WTG.

The number of simultaneously running WTGs depends by whole number of turbines from wind farm and by its FOR. Taking into account that the WTGs from a wind farm are alike, the each outage of a WTG from the farm may be counted using the Binomial distribution. Based on these issues, the authors have developed, in other paper [15], an analytical expression of the probability density function for the output power of a wind farm with N identical turbines:

[ ] [ ][ ]

[ ][ ]

[ ]⎪⎪⎪⎪⎪

⎩

⎪⎪⎪⎪⎪

⎨

⎧

⋅=Β⋅ℜ

⋅<<⋅−

Β⋅

=

Β⋅ℜ+Β

= ∑

∑

=

=

ratedWF

ratedWFrated

N

kWFWTk

WF

N

k

WFN

WF

PkPforAVNk

PkPPkfor

AVNkPf

Pfor

AVNkAVN

Pf

,/2)1(

,/)(

0

,/1,/0

)(1

1

(4) where: • [ ])()( incutWoutcutW vFvF −− −−=ℜ 11 , is the cumulative

distribution function to zero power; • )()(2 ratedWoutcutW vFvF −=ℜ − , is the cumulative

distribution function for Prated power;

• ( )⎟⎟⎠

⎞⎜⎜⎝

⎛+

⋅⋅−

⋅⎟⎟⎠

⎞⎜⎜⎝

⎛⋅−

= −−−

incutrated

incutratedW

rated

incutratedWTk v

PkPvvf

PkvvPf )(

is the density function of the k wind turbine generators simultaneously running, evaluated with the wind density function fW;

• ratedoutcutratedincut Pvvv ,,, −− are the parameters of wind turbine generators;

• [ ] [ ] ,/ kNkkN (FOR)AV= CAVNk −⋅Β is the binomial

distribution, applicable for counting the probability of k wind turbines running from whole N wind turbines of system;

• fW is the probability density of wind speed. A Matlab-Simulink program, based on the previous models

and using convolution technique, was developed by the authors to evaluate the generation adequacy indices and capacity benefit.

IV. EFFECT OF WIND TURBINE PARAMETERS ON GENERATION ADEQUACY

To illustrate the effect of wind turbine parameters on generation capacity adequacy, a hybrid test system was created based on Roy Billinton Test System (RBTS) and the load model of IEEE-RTS [16]. The IEEE Reliability Test Systems (IEEE-RTS) was developed by the Subcommittee on the Application of Probability Methods in the IEEE Power Society, to provide a common test system which could be used for comparing the results obtained from different methods.

As mentioned, the load model of IEEE-RTS is utilised in the paper. All the data of IEEE-RTS is given as percentages of the annual, weekly and daily peak of load. The hourly load is developed by multiplying the load model per unit values with the annual peak load. For 185 MW peak, the chronological load profile for 8736 hours based on given weekly, daily and hourly pattern is obtained. After all 8736 hourly loads have been computed, the load probability function associated to characteristic load curve is obtained, as is presented in figure 2.

0 20 40 60 80 100 120 140 160 180 2000

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

Loads [MW]

Pro

babi

lity

Fig. 2. The load probability function

1226

The RBTS has 11 generating units ranging from 5 MW to

40 MW, with a total capacity of 240 MW. Generation system data contains the type of generating units, capacity (MW) and number of each units, respectively reliability data like mean time to failure and repair (in hour). After convolved all N=11 discrete random variable of the generating units, a discrete random variable of conventional generating system is obtained, as it is presented in figure 4.a.

0 50 100 150 200 2500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Available conventional generating power [MW]

Pro

babi

lity

Fig. 3. Probability functions for conventional generators

For the wind output power modelling, a real wind speed

measurement is utilized in the paper. The wind speed database is collected from the north-east area of Romania, for a measurement interval to one hour for the year 2008. The figures 4.a,b present the wind speed collected to wind station height (10m) and adjusted to the hub wind turbine height (80m). The probability density and the density function fitted for different wind speed values in 1 m/s steps were evaluated. The probability distribution function used to fitting is a Weibull distribution with scale parameter α=4.82253 m/s and a shape parameter β=1.8656.

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

5

10

15Wind speed to measured height (10m)

time (hr)

win

d sp

eed

(m/s

)

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

10

20

30Wind speed to 80 m height

time (hr)

win

d sp

eed

(m/s

)

Fig. 4.a,b – Wind speed data base from the north-east area of Romania,

to 10m, respectively 80 m height.

The probabilistic model (4) presented in this paper was applied to a wind farm composed by N=20 wind turbines. The wind turbines chosen for analysis are manufactured by GE Energy, and their technical specifications are presented in table 1 (case no.1). For each wind turbine generator will consider the FOR=0.04 and will be placed in analyzed area, for which the authors has records of wind speed. The probability function, for 20 × 1.5xle-GE wind turbines is presented in figure 5.

-4 0 4 8 12 16 20 24 28 32

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

WTG output power [MW]P

roba

bilit

y

Fig. 5. Probability functions for wind turbine generators

Using the developed program in Matlab, the authors has been analysed the effects of different cut-in, rated and cut-out wind speeds on the basic adequacy indices. The effects of the WTG parameters of the generation adequacy are shown in figure 6. In the same coordinate system is shown the dependence of adequacy indices for various wind speeds values around of WTG parameters (cut-in, rated, cut-out speeds).

v-3 v-2 v-1 v v+1 v+2 v+30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

wind speed step (m/s)

LOLE

(h/y

r)

20WT 1.5xle-GE (vcut-in=3.5 m/s, vrat=11.5 m/s, vcut-off=20 m/s)

v=Vcut-off=20m/s

v=Vrat=11.5 m/s

v=Vcut-in = 3.5 m/s

Fig. 6. Effect of Wind turbine parameters on LOLE

It has been shown that the cut-in wind speed has a

significant effect on the adequacy indices and on load carrying capacity benefit, also. The Loss of Load Expectation

1227

and the Loss of Energy Expectation increase approximately linearly as the cut-in wind speed increases.

It has been shown that the rated wind speed has a relatively small effect on the adequacy indices. The rated wind speed growth leads to the adequacy indices growth, but this effect is less significant than that of the cut-in wind speed

It has been shown that the cut-out wind speed has no effect on the adequacy indices. The cut-out wind speed is a safety parameter and is usually large. For relatively few times the instantaneous wind speed at a particular area will be greater than the cut-out speed. The selection of the cut-out speed parameter is therefore less important than that of the cut-in and the rated wind speed parameters.

V. SELECTING THE OPTIMAL WTG

As has been shown, the wind turbine design parameters, particularly cut-in speeds, affect the generation adequacy indices and the load carrying benefit. Selection of a suitable wind turbine, for a specific area with a wind profile, is therefore important in order to achieve the maximum capacity and energy benefit from a WES.

The methodology for selecting a suitable WT is based on investigation of all possible cases to adding various WTG types to the original power system [17,18]. These possible cases are investigated in terms of effects on the adequacy indices and on load carrying benefits and from these is selected the case that improves the best solutions.

The wind turbines chose for analysis of effects on adequacy indices are manufactured by four international companies, namely General Electric Energy, Vestas Wind Systems, Nordex AG and Enercon Energy [19-22].

The table I show different types of turbines, with various values of rated power and different parameters, which will be installed in the WES and added to RBTS.

TABLE I

TECHNICAL DATA OF WIND TURBINES Cases Turbine Model Rated

power (MW)

Cut-in speed (m/s)

Rated speed (m/s)

Cut-off speed (m/s)

Hub Heights

(m) 1 1,5xle - GE 1.5 3.5 11.5 20 65-80 2 1,5 sle - GE 1,5 3.5 14 25 65-80 3 1,5 s - GE 1,5 4 12 25 65-85 4 2,5 xl - GE 2,5 3.5 12.5 25 75-100 5 Enercon E-82 2 2.5 13 28 78-138 6 V90-2 2 4 12 25 80-105 7 V90-3 3 3.5 15 25 80-90 8 N70-1,5 1,5 3 13 25 80-100 9 N80-2,5 2,5 3 13 25 80

It is assumed that the conventional system is expanded with

30 MW installed in WES. The same installed power may be obtained from 20 WTs by 1.5 MW rated power, 15 WTs by 2 MW, 12 WTs by 2.5 MW or 10 WTs by 3 MW.

The RBTS can be expanded in different ways by adding the same 30 MW capacity from previous combinations between number and rated power of WTGs. The following combinations of adding 30 MW wind power to the original RBTS was investigated: 20 WTs from cases 1, 2, 3 and 8; 15

WTs from cases 5 and 6; 12 WTS from cases 4 and 9, respectively 10 WTS from case 7.

The relative benefits of adding different types of WTG to the RBTS have been analyzed. A comparison of the system reliability for different capacity addition cases is shown in figure 6.

170 175 180 185 190 195 2000

0.5

1

1.5

2

2.5

3

3.5

4

peak (MW)

LOLE

(h/y

r)

without WT

5 8

412 7

63

9

Fig. 7. Effect of Wind turbine parameters on LOLE

For a better comparison of the WTG types, the values of

adequacy and capacity benefit indices are given in Table II. TABLE II

RELIABILITY INDICES FOR VARIOUS EXPANSIONS OF RBTS Cases No of

WT LOLE (h/yr)

LOEE (MWh/yr)

PLCCwt (MW)

IPLCC (MW)

LCCBR (%)

1 20 0.5232 4.7292 195.4 10.4 34.67 2 20 0.5352 4.8361 193.55 8.55 28.5 3 20 0.6111 5.5306 192 7 23.33 4 12 0.5282 4.7740 193.67 8.67 28.9 5 15 0.3556 3.1991 199.15 14.15 47.16 6 15 0.6112 5.5315 191.95 6.95 23.16 7 10 0.5403 4.8816 193.5 8.5 28.33 8 20 0.4427 3.9936 195.92 10.92 36.4 9 12 0.4430 3.9958 195.9 10.9 36.33

The suitable wind turbine will be the one with the lowest

adequacy indices (LOLE and LOEE) and the largest peak load carrying capability index (IPLCC).

Reliability indices comparisons show that the maximum improvement occurs by adding a wind farm composed by 15 Enercon-82 wind turbines (case no.5), while the minimum improvement occurs by adding a wind farm composed from 15, V90-2 turbines (case no.6). Enercon-82 turbine has the lowest LOLE and EENS and the highest IPLCC.

The capacity benefit obtained from the Enercon-82 solution is double that the V90-2 solution. It should be noted that the both solutions use the same number of turbines, with the same rated power (2 MW). It can be deduced that the number of turbines and turbine rated power has no effect on reliability indices.

This idea is sustained by cases 8 and 9, respectively 2,4 and 7, which have close values of the reliability indices, although

1228

the number and rated power of WTG are completely different.

Thus, it can be concluded that the most suitable wind turbine for analyzed area, it is that characterized by lower cut-in and rated wind speeds. The wind turbine cut-off speed, the number and rated power of WTGs from wind farm are not criteria in the optimal selection of wind turbines.

VI. CONCLUSION

Integration of wind energy system is an important activity in the planning process of the electric power system. The probabilistic methods are the recommended solution for planning process, since they can take into account the wind power uncertainty.

In the paper, a probabilistic method to select the suitable WT for a specific are is presented. The objective of method is to minimize the adequacy indices and to maximize the load carrying capability index.

The electric energy output of a wind turbine for a specific are depends by many factors. These factors include the wind speed conditions at the area, and the characteristics of the wind turbine generator.

The case studies show that turbine cut-in wind speed has a significant effect on the adequacy indices and carrying capability benefit of a generating system while the cut-out wind speed has almost no effect. It can be deduced that the number of turbines and turbine rated power has no effect on reliability indices.

Significant capacity benefits can be obtained by selecting appropriate wind turbine parameters evaluated in terms of wind speed profile.

REFERENCES

[1] Tony Burton, David Sharpe, Nick Jenkins, Ervin Bossanyi, Wind Energy Handbook, John Wiley & Sons 2001.

[2] Thomas Ackermann, Wind Power in Power Systems, Ed. John Wiley & Sons 2005.

[3] Z. M. Salameh and I. Safari, “Optimum windmill –site matching,” IEEE Trans. Energy Convers., vol. 4, no. 7, pp. 669-675, 1992.

[4] R. Billinton, R. Allan, Reliability evaluation of power systems, 2nd Edition, Plenum Press, New York, 1996.

[5] R. Billinton, W. Y. Li, Reliability assessment of electrical power systems using Monte Carlo method, Plenum Press, New York, 1994.

[6] Billinton R, Gao Y. “Multistate wind energy conversion system models for adequacy assessment of generating systems incorporating wind energy”. IEEE Trans Energy Convers 2008;23(1):163–70.

[7] Wang P, Billinton R. “Reliability benefits analysis of adding WTG to a distribution system”. IEEE Trans Power Syst 2001;16(2):134–9.

[8] Billinton R, Chen H. “Assessment of risk-based capacity benefit factors associated with wind energy conversion systems” IEEE Trans Power Syst 1998; 13(3):1191–6.

[9] Bagen, R. Billinton and R. Karki, “Reliability evaluation of isolated solar-diesel power systems using a time series simulation model”, Proceedings of the 28th Annual National Conference of the Solar Energy.

[10] Roy Billinton, Bagen, “Reliability Considerations in the Utilization of Wind Energy, Solar Energy and Energy Storage in Electric Power“ Systems 9th International Conference on Probabilistic Methods Applied to Power Systems KTH, Stockholm, Sweden – June 11-15, 2006

[11] Leite da Silva, A.M.; Pazo Blanco, F.A.F.; Coelho, J. “Discrete convolution in generating capacity reliability evaluation-LOLE

calculations and uncertainty aspects Power Systems”, IEEE Transactions on Volume 3, Issue 4, Nov 1988 Page(s):1616 – 1624

[12] Isaac Y. F. Lun, Joseph C. Lam, “A study of Weibull parameters using long-term wind observations” Renewable Energy Journal, Volume 20, Issue 2, June 2000, pages 145-153.

[13] J.A. Carta, P. Ramırez. “A review of wind speed probability distributions used in wind energy analysis. Case studies in the Canary Islands”. Renewable and Sustainable Energy Reviews, Volume 13, Issue 5, June 2009, Pages 933-955. www.elsevier.com/locate/rser.

[14] Mohammad A. Al-Fawzan “Algorithms for Estimating the Parameters of the Weibull Distribution” Interstat journal. October 2000. http://interstat.statjournals.net/YEAR/2000/articles/0010001.pdf

[15] C. Nemes, F Munteanu, “Probabilistic approach of the generated power of a wind turbine” Buletinul Institutului Politehnic din Iasi, fasc.IV, 2009, pag 109-116.

[16] R. Billinton, S. Kumar, N. Chowdhury, K. Chu, K. Debnath, L. Goel, E. Khan, P. Kos, G. Nourbakhsh and J. Oteng-Adjei, “A reliability test system for educational purposes-basic data”, IEEE Transactions on Power Systems, Vol. 4, No.3, 1989, pp.1238-1244.

[17] H. Chen, R. Billinton, “Determination of the optimum site-matching wind turbine using risk-based capacity benefit factors,” IEE Proc-Gener. Transm. Distrib., vol. 146, no. 1, pp. 96-100, Jan. 1999.

[18] Billinton R, Chen H. “Effect of windturbine parameters on the capacity adequacy of generating systems using wind energy” Conference on communications, power and computing WESCANEX’97 Proceeding; 1997.p. 47–52.

[19] http://www.ge.com/products_services/energy.html [20] http://www.vestas.com/en/about-vestas.aspx [21] http://www.nordex-online.com/en [22] http://www.enercon.de/en/_home.htm

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Optimal Selection of Wind Turbine For a Specific Area

Author(s): Nemes, C (Nemes, Ciprian)1; Munteanu, F (Munteanu, Florin)1

Book Group Author(s): Transilvania Univ Brasov, Fac Elect Engn & Comp Sci

Source: OPTIM 2010: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ONOPTIMIZATION OF ELECTRICAL AND ELECTRONIC EQUIPMENT, PTS I-IV Book Series:Proceedings of the International Conference on Optimization of Electrical and Electronic Equipment Pages: 1224-1229 Published: 2010

Times Cited: 0 (from Web of Science)

Cited References: 18 [ view related records ] Citation Map

Conference: 12th International Conference on Optimization of Electrical and Electronic EquipmentLocation: Brasov, ROMANIA Date: MAY 20-21, 2010Sponsor(s): IEEE, IAS; IEEE, PELS; IEEE, IES

Abstract: In this paper a method for reliability estimation of power systems including wind energygenerators is presented. The method is useful to compare different wind turbine generator typesand to establish the suitable wind generator type in a given area. The wind generator parameters tobe considered for an optimal selection are also presented in details, having in view the improvementof system reliability indices concerning mainly the available generated power.

Accession Number: WOS:000291967300183

Document Type: Proceedings Paper

Language: English

KeyWords Plus: CAPACITY BENEFIT FACTORS; SYSTEM; ENERGY

Reprint Address: Nemes, C (reprint author), Gheorghe Asachi Tech Univ Iasi, Iasi, Romania.

Addresses:1. Gheorghe Asachi Tech Univ Iasi, Iasi, Romania

E-mail Address: cnemes@ee.tuiasi.ro

Publisher: TRANSILVANIA UNIV PRESS-BRASOV, BD EROILOR NR 9, BRASOV, RO-500030,ROMANIA

Web of Science Categories: Engineering, Electrical & Electronic; Operations Research &Management Science

Research Areas: Engineering; Operations Research & Management Science

IDS Number: BVN54

ISSN: 1842-0133

Times Cited: 0

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