1&:It!J Proceedings of the "International conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET20/3)


 Russell Wilkerson
 9 months ago
 Views:
Transcription
1 1&:It!J Proceedings of the "International conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET20/3) L = t:i "" organized by Sathyabama University, Chennai, India in association with DRDO, New Delhi, India, 24 1h _261 h, July, 2013 Self Adaptive Firefly Algorithm Based Transmission Loss Minimization using TCSC RSelvarasu #l, CChristober Asir Rajan * 2 " Department ofeee, JNTUH, Hydera bad, India * Department of EEE, Pondicherry Engineering College, Puducherry, India I INTRODUCTION In recent years, the demand for electrical energy is exponentially increasing The construction of new generation system, power transmission networks can solve these demands However, there are some limitations to construct new system They involve installation cost, environment impact, political, large displacement of population and land acquisition One of the alternative solutions to respond the increasing demand is by minimization of transmission loss using Flexible Alternating Current Transmission Systems (FACTS) devices The FACTS is a concept proposed by NGHingorani [1] as a well known term for higher controllability in power system by means of power electronic devices Better utilization of an existing power system capacity by installing FACTS devices has become essential in the area of ongoing research FACTS devices have the capability to control the various electrical parameters in transmission network in order to achieve better system performance F ACTS devices can be divided in to three categories Shunt Connected, Series connected and combination of both [2] The Static Var Compensator (SVC) and Static Synchronous Compensator (ST A TCOM) are belongs the shunt connected devices and are in use for a long time in various places Consequently, they are variable shunt reactors which inject or absorb reactive power in order to control the voltage at a given bus [3]Both Thyristor Controlled Series Compensator (TCSC) and Static Synchronous Series Compensator (SSSC) are series connected devices The TCSC and SSSC mainly control the active power in a line by varying the line reactance They are in operation at a few places but are still in the stage of development [45]Unified Power Flow Controller (UPFC) is belongs to Combination of Shunt and Series devices UPFC is able to control active power, reactive power and voltage magnitude simultaneously or separately [6] Optimal location of various types of FACTS devices in the power system has been experienced using different Metaheuristic algorithm such as Genetic Algorithm (GA),Simulated 2asir AbstractThis paper presents the use of Thyristor annealing (SA),Ant Colony Optimization (ACO), Bees Controlled Series Compensator (TCSC) to minimize the algorithms (BA), Differential Evolution (DE), and Particle transmission loss in power system network SelfAdaptive Swarm Optimization (PSO) etc [7]Optimal location of multi Firefly Algorithm is proposed to identify the optimal type FACTs devices in a power system to improve the location and parameter of TCSC To validate the loadability by means of Genetic Algorithm has been proposed algorithm, simulations are performed on IEEE successfully implemented [9]PSO has been applied to 14bus and IEEE 30bus system using MATLAB software determine the optimal location of FACTS devices, reactive package Analyzing the simulation results the identified power and voltage control [1011] PSO has been proposed to location and parameter of TCSC is able to minimize the improve the power system stability by determining the optimal transmission loss in the power system network location and controller design of STATCOM [12] PSO has KeywordsFirefly Algorithm, Loss Minimization, been proposed to select the optimal location and setting Optimal Location, TCSC parameter of SVC and TCSC to mitigate small signal oscillations in multi machine power system [13] Bacterial Foraging algorithm has been used to find the optimal location of UPFC devices with objectives of minimizing the losses and improving the voltage profile [14] Firefly Algorithm has been developed by XinShe Yang [78] which is found superior over other algorithms in the sense that it could handle multi modal problems of combinational and numerical optimization more naturally and efficiently [15] It has been then applied by various researchers for solving various problems, to name a few: economic dispatch [1617], fault identification [18], crypto analysis of knapsack problems [19], scheduling [20], Unit commitment [21] and image compression [22] etc In this paper Self Adaptive Firefly Algorithm is proposed to identify the optimal location and parameter of TCSC, which minimizes the transmission loss in the power system network Simulations are performed on IEEE 14bus and IEEE 30bus system using MA TLAB software package Simulations results are presented to demonstrate the effectiveness of the proposed approach II TCSC MODEL The Thyristor Controlled Series Compensator (TCSC) is capacitive reactance compensator, which consists of a series capacitor bank shunted by a thyristor controlled reactor in order to provide a smoothly variable series capacitive reactance [2] The TCSC can be connected in series with the transmission line to compensate the inductive reactance of the transmission line The reactance of the TCSC depends on its compensation ratio and the reactance of the transmission line where it is located The TCSC model is shown in Figl The TCSC modeled by the reactance, Xn:sc: as follows, X ij = Xline + Xrcsc (1) (2) /13/$ IEEE 683
2 I Proceedings of the "International Conference on Advanced Nanomaterials & tr Emerging Engineering ech logies" (ICANMEET20J3) organized by Sathyabama Umverslty, Chennal, IndIa III association with DRDO, New Deihl, IndIa, 2426, July, 2013 La III;:J where Xline is the reactance of the transmission line and rrcsc is the compensation factor of the TCSC specific optimization problem However, typically a population size of 20 to 50 is used for PSO and Firefly Algorithm for most applications [10, 25] Each ni h firefly is denoted by a vector (3) FigITCSC Model III FIREFLY ALGORITHM A Classical Firefly Algorithm Firefly Algorithm is a recent nature inspired meta heuristic algorithms which has been developed by Xin She Yang at Cambridge university in 2007[7] The algorithm mimics the flashing behavior of fireflies It is similar to other optimization algorithms employing swarm intelligence such as PSO But Firefly Algorithm is found to have superior performance in many cases [8]1t employs three ideal rules First, all fireflies are unisex which means that one firefly will be attracted to other fireflies regardless of their sex Secondly, the degree of the attractiveness of a firefly is proportional to its brightness, thus for any two flashing fireflies, the less bright one will move towards the brighter one More brightness means less distance between two fireflies However if any two flashing fireflies are having same brightness, then they move randomly Finally the brightness of a firefly is determined by the value of the objective function For a maximization problem, the brightness of each firefly is proportional to the value of the objective function In case of minimization problem, the brightness of each firefly is inversely proportional to the value of objective function Firefly Algorithm initially produces a swarm of fireflies located randomly in the search space The initial distribution is usually produced from a uniform random distribution The position of each firefly in the search space represents a potential solution of the optimization problem The dimension of the search space is equal to the number of optimizing parameters in the given problem The fitness function takes the position of a firefly as input and produces a single numerical output value denoting how good the potential solution is A fitness value is assigned to each firefly The brightness of each firefly depends on the fitness value of that firefly Each firefly is attracted by the brightness of other fireflies and tries to move towards them The velocity or the pull a firefly towards another firefly depends on the attractiveness The attractiveness depends on the relative distance between the fireflies It can be a function of the brightness of the fireflies as well A brighter firefly far away may not be as attractive as a less bright firefly that is closer In each iterative step, Firefly Algorithm computes the brightness and the relative attractiveness of each firefly Depending on these values, the positions of the fireflies are updated After a sufficient amount of iterations, all fireflies converge to the best possible position on the search space The number of fireflies in the swarm is known as the population size, N The selection of population size depends on the where nd the number of decision variables The search space is limited by the following inequality constraints xv(min) xv xv(max) v = 1,2,,nd (4) Initially, the positions of the fireflies are generated from a uniform distribution using the following equation Xll = XV (min) + (x v (max) x v (min) ) x rand (5) Here, rand is a random number between 0 and I, taken from a uniform distribution The initial distribution does not significantly affect the performance of the algorithm Each time the algorithm is executed, the optimization process starts with a different set of initial points However, in each case, the algorithm searches for the optimum solution In case of multiple possible sets of solutions, the algorithm may converge on different solutions each time But each of those solutions will be valid as they all will satisfy the requirements The light intensity of the ni h firefly, n is given by The attractiveness between ni h and n th firefly, f3 m, n by Pm,n = (Prm;x,m,n Pmin,m,n ) exp( rmrm,n 2) + Prnil,"m,n (7) where r lti,n is given is Cartesian distance between ni h and n th firefly The value of {J,nin is taken as 02 and the value of Pmax is taken as 1 r is another constant whose value is related to the dynamic range of the solution space The position of firefly is updated in each iterative step If the light intensity of n th firefly is larger than the light intensity of the ni h firefly, Jh th then the m firefly moves towards the n firefly and its motion at J( h iteration is denoted by the following equation: xm(k) = xm(kl) + It,n (xm(kl)xm(kl)) +a(ramo5) (8) (9) 684
3 I Proceedings of the "International Conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET20J3) organized by Sathyabama University, Chennai, India in association with DRDO, New Delhi, India, 24 t h _26tl" July, 2013 LiiII :J_ The random movement factor a is a constant whose value depends on the dynamic range of the solution space At each iterative step, the intensity and the attractiveness of each firefly is calculated The intensity of each firefly is compared with all other fireflies and the positions of the fireflies are updated using (9) After a sufficient number of iterations, all the fireflies converge to the same position in the search space and the global optimum is achieved B Self Adaptive Firefly Algorithm In the above narrated Firefly Algorithm, each firefly of the swarm travel around the problem space taking into account the results obtained by others, still applying its own randomized moves as well The random movement factor (a) is very effective on the performance of Firefly Algorithm A large value of a makes the movement to explore the solution through the distance search space and smaller value of a tends to facilitate local search In this paper the random movement factor (a) is dynamically tuned in each iteration The influence of other solutions is controlled by the value of attractiveness (7), which can be adjusted by modifying three parameters a, Pmin and rln general the value of Pmax should be used from 0 to I and two limiting cases can be defined: The algorithm performs cooperative local search with the brightest firefly strongly determining other fireflies positions, especially in its neighborhood, when /l,nax = I and only noncooperative distributed random search with Pmax = o On the other hand, the value of r determines the variation of attractiveness with increasing distance from communicated firefly Setting r as 0 corresponds to no variation or attractiveness is constant and conversely putting r as 00 results in attractiveness being close to zero which again is equivalent to the complete random search In general r in the range of 0 to I 0 can be chosen for better performance Indeed, the choice of these parameters affects the final solution and the convergence of the algorithm Each firefly with nd decision variables in the Firefly Algorithm will be defined to encompass nd +3Firefly Algorithm variables in a selfadaptive method, where the last three variables represent am ' Pminm and rm A firefly can be represented as (10) To achieve the better utilization of an existing power system, the optimal location and parameter of TCSC to be identified in the power transmission network to minimize the total real power loss The objective of this paper is to identify the optimal location and parameter of the TCSC which minimize the real power loss Rrrltll? POWY S)8lemIUa Sdedtll?[XpiatiOlsizeNan1Mlxinwnl1lltJierqIteratirn;jrrcrYM!llJ?lll?drr:k C rerute til? initid [XpictiOl Wile ernin:tioli'f!quirr:i11!n1sa"e ndm!l:) ch jrrm=i:n Alter til? ftemch!n, a, mn arl r a:rrrdirg to m/h jrrljlywiws!itn til? lax! flcm' Cmpde til? Red JXMf!t' lass Cak:uJde n Frr n=i:n Alter tll? ftemch!n=adirgto nthfirljlywiws!itn til? lax! flcm' Cmpde til? Red JXMf!t' lass Cak:uJde In If1m<ln Cmpute ':nj' U5irg (8) wzae /J",,, usirg(7j MJu? nl''firifly t(mltm rt jrrifly thuffz (51) en1if en1jrrn en1jrrm &irk til? jrrljlie5 an1jind til? CUffed!:eft FniWiJe Fni Fig2Psuedo Code of Firefly Algorithm A Objective function The objective is to minimize transmission loss, which can be evaluated from the power flow solution [14], and written as nl Min oss = L G/( 2 + V/  2 Vj cos 0ij) (11) 1=1 where oss =Real power loss, G1 =Conductance of rh line, =Voltage magnitude at bus i, =Voltage magnitude at bus j, S =Voltage angle at bus i and j and nl =Total If number of transmission lines Each firefly possessing the solution vector, am ' Pmin,m and rm undergo the whole search process During iteration, the FA produces better offsprings through (7) and (9) using the parameters available in the firefly of (10), thereby enhancing the convergence of the algorithm The basic steps of the Firefly Algorithm can be summarized as the pseudo code which is depicted in Fig 2 B Pro blem constraints The equality constraints are the load flow equation given by Pui  PDi = PJV, 0) (12) QUi  QDi = Q (V, 0) (13) where Pc;i and QUi represent the real and reactive power injected by the generator at bus i, respectively PDi IV PROBLEM FORMULATION 685
4 I Proceedings of the "International Conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET20J3) organized by Sathyabama University, Chennai, India in association with DRDO, New Delhi, India, 24th _26tl" July, 2013 LiiII :J_ and QJ)i represent the real and reactive power drawn by the load at bus i, respectively Two inequality constraints are considered as follows The first constraint includes reactive power generation at PV buses Q,ṃin Q, max Q, (14) (JI Cd e,f were h Q min Q max Gi and are the upper and lower limit of Gi reactive power source i The second constraint represents the rating of TCSC O8Xline XiCSC O2XlineP U (15) The Self Adaptive firefly algorithm based optimal location of TCSC problem is defined as xm ={, 'XC',{"l'l,ATin,m' rnj (LM' 'XC':'t"M'l,fJnin,m' rnj (LN' I1CY',N' ' fjrrin,n' YN)} (16) where LM is the Line location of the MhTCSC, Yrcsc,M is the Compensation factor of M h TCSC The Self Adaptive Firefly Algorithm searches for optimal solution by maximizing light intensity I m ' like fitness function in any other stochastic optimization techniques The light intensity function can be obtained by transforming the power loss function into I m function as 1 MaxIm =  (17) 1 + oss A population of firefly is randomly generated and their intensity is calculated using (6) Based on the light intensity, each firefly moved to the optimal solution through (9) and the iterative process continues till the algorithm converges V SIMULATION RESULTS AND DISCUSSIONS The simulation results for optimal location and parameter of Thyristor Controlled Series compensator to minimize the transmission loss in the power system network were obtained and discussed in this section The line data and bus data for the IEEE 14bus and IEEE 30bus are taken from [24] Case 1: IEEE 14 bus system In first case IEEE 14 bus system has been considered to identify the optimal location and parameter of the TCSC to minimize the real power loss IEEE 14 bus system has 20 transmission lines, five generator buses (bus no 1,2,3,6 and 8) and rests are load buses Simulations are carried out for different number of TCSC without considering the installing cost The simulation results in terms of the locations and the TCSC parameters and the resulting loss are presented in table I it is observed from the table I that the identified location of TCSC minimizes the real power loss When three TCSC is considered real power loss considerably reduced from MW to MW If four TCSC is considered the real power loss reduction is insignificant from the installation cost point of view It is thus concluded that three TCSC devices are adequate to achieve the desired goal of minimizing the loss TABLE I OPTIMAL LOCATION, PARAMETER of TCSC and REAL POWER LOSS for IEEE 14 BUS SYSTEM Real power Type of No of loss Locations YICSC System TCSC in MW (Line No) in pu IEEE bus TABLE II OPTIMAL LOCATION, PARAMETER of TCSC and REAL POWER LOSS Type of System for IEEE 30 BUS SYSTEM Real power No of Locations loss YTCSC TCSC (Line No) in MW in pu IEEE bus
5 I Proceedings of the "International Conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET2013) organized by Sathyabama University, Chennai, India in association with DRDO, New Delhi, India, 24 t h LiiII _26tl" July, 2013 :J_ Case 2: IEEE 30 bus system In this case IEEE 30 bus system has been considered to identify the optimal location and parameter of the TCSC to minimize the real power loss IEEE 30 bus system has 41 transmission lines, six generator buses (bus no I, 2, 5, 8, 11 and 13) and rests are load buses Simulations are carried out for different number of TCSC without considering the installing cost The simulation results in terms of the locations and the TCSC parameters and the resulting loss are presented in table II It is observed from the table II that the identified location of TCSC minimizes the real power loss When six TCSC is considered real power loss considerably reduced from MW to MW It is thus concluded that six TCSC devices are adequate to achieve the desired goal of minimizing the loss A Parameter a/the Adaptive Firefly Algorithm The population size N for IEEE 14 bus system and IEEE 30bus system are taken as 30 The maximum number of Iterations considered as 200 The random movement factor a are tuned during each iteration The initial value of a is set to 05 The attractiveness parameter fj is varied from fjmin to "ax The value of fjmin is taken as 02 and the value of "ax is taken as 1 The absorption parameter r is taken as 1 and it is tuned in all iteration It has to be pointed out that the performance of the entire metaheuristic optimization algorithm is very dependent on the tuning of their different parameters A small change in the parameter may result in a large change in the solution of these algorithms Self adaptive Firefly Algorithm is a powerful algorithm which efficiently tuned all the parameters to obtain the global or near global optimal solution VI CONCLUSION The optimal location of FACTS devices play a vital role in achieving the proper functioning of these devices However this paper made an attempt to identify the optimal location and parameter of TCSC which minimizes the transmission loss in the power system network using Self Adaptive Firefly algorithm Simulations results are presented for IEEE14bus and IEEE30 bus systems Results have shown that the identified location of TCSC minimize the transmission loss in the power system network With the above proposed algorithm it is possible for utility to place TCSC devices in transmission network such that proper planning and operation can be achieved with minimum system losses REFERENCES [1] N G Hingorani and LGyugyi,Understanding FACTS Concepts and Technology of Flexible AC Transmission Systems, New York:IEEE Press,2000 [2] RMMathur and RKVarma, Thyristorbased FACTS Controllers for Electrical Transmission Systems, Piscataway: IEEE Press,2002 [3] DrHAmbrizperez,EAchaand,CRFuerteEsquivel "Advanced SVC models for newton raphson load flow and newton optimal power flow studies,"ieee Systems, vol 15,pp ,2000 Transaction on Power [4] TOrfanogianni, "A Flexible software environment for steady state power f10w optimization with series FACTS devices,"diss ETH Zurich 2000,13689 [5] E YLarsen,K Clark, SAMiske,Jr,andJ Urbanek, "Characteristic and rating considerations of thyristor controlled series compensation," IEEE Transaction Power Delivery,voI9,pp ,1994 [6] DrLGyugyi, "Unified power flow controler concept for flexible AC transmission system,"in lee proceedings, vol I 39,no4pp I, July, 1992 [7] X S Yang,NatureInspired MetaHeuristic Algorithms, 2nd ed, Beckington, Luniver Press,20 I O [8] X SYang, Firefly algorithms for multimodal optimization, Stochastic algorithms: Foundations and applications, SAGA 2009, LNCS, Berlin, Germany: SpringerVerlag, 2009, vol 5792, pp [9] SGerbex, RCherkaom, and AJGermond, "Optimal location of multi type FACTS devices in a power systems by means of genetic algorithms," IEEE Transaction on Power SystemsvoI16, no 3, pp , 2001 [10] M Saravanan, S M R Slochanal, P Venkatesh, J P S Abraham, "Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability," Electrical Power System Research, vol77, pp , 2007 [11] Hirotaka Yoshida, Kenichi Kawata, Yoshikazu Fukuyama,"A Particle swarm optimization for reactive power and voltage control considering voltage security assessment," IEEE Transaction on Power Systems, vo1l5,no4, ppl , Nov 2001, [12] Sidhartha Panda, Narayana Prasad Padhy, "Optimal Location and controller design of ST ATCOM for power system stability improvement using PSO," Journal of the Franklin Institute, voi345, pp , Mar 2008, [13] D Mondal, A Chakrabarti, A Sengupta, "Optimal placement and parameter setting of SVC and TCSC using PSO to mitigate small signal stability problem," International Journal of Electrical Power & Energy Systems, voi42,nol,pp , Nov 2012 [14] M Senthil Kumar, P Renuga, "Application of UPFC for enhancement of voltage profile and minimization of losses using fast voltage stability index(fvsi), "Archives Of Electrical Engineering Journal, vol61, no2, pp , Jun 2012 [15] X S Yang,"Firefly algorithm stochastic test functions and design optimization," International Journal of Bioinspired Computation, vol 2,pp 7884,2010 [16] T Apostolopoulos, and A Vlachos, "Application of the Firefly algorithm for solving the economic emissions load dispatch problem," International Journal of Combinatorics, vol2011, no523806, p23, 2011 [17] XS Yang, S S Hosseini, and AH Gandomi, "Firefly algorithm for solving nonconvex economic dispatch problems with valve loading eflect," Applied Soft Computing, vol 12, Issue 3, pp , Mar 2012 [18] R Falcon, M Almeida, and A Nayak, "Fault identification with binary adaptive firef1ies in parallel and distributed systems," in proc IEEE Congress on Evolutionary Computation, Jun 2011pp [19] S Palit, S Sinha, M Molla, A Khanra, and M Kule, "A Cryptanalytic attack on the knapsack cryptosystem using binary firefly algorithm," in proc2nd international Conference on Computer and Communication Technology (ICCC!), Sep 2011, pp [20] K Chandrasekaran and S P Simon, "Demand response scheduling in SCUC problem for solar integrated thermal system using firefly algorithm," in proc iet Conference on Renewable Power Generation (RPG 2011), 2011, pp 18 [21] K Chandrasekaran and S P Simon, "Network and reliability constrained unit commitment problem using binary real coded 687
6 I Proceedings of the "International Conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET20J3) organized by Sathyabama University, Chennai, India in association with DRDO, New Delhi, India, 24 t h _26tl" July, 2013 LiiII :J_ firefly algorithm," International Journal of Electrical Power & Energy Systems, vo143, Issuel, pp , Dec2012 [22] MH Homg and R J Liou, "Multilevel minimum cross entropy threshold selection based on the firefly algorithm," Expert Systems with Applications, vol 38, Issue 12, pp , 20 II [23] EAcha,CRFuerteEsquivel, Hugo Ambrizperz, CAugeless Cameto, FACTS Modelling and simulation in power network, 1 sl ed New York: John Wiley & Sons Ltd, 2004 [24] Power Systems Test Case, The University of Washington Archive, [Online ]Available: ee washingtonedu/research/pstca/, [25] Taher Nlknam, Rasoul AzizipanahAbarghooee, and Alireza Roosta, "Reserve Constrained Dynamic Economic Dispatch:A New Fast SelfAdaptive Modified Firefly Algorithm," IEEE System Journal, vol6, no 4, Dec
Application of GA for Optimal Location of FACTS Devices for Steady State Voltage Stability Enhancement of Power System
I.J. Intelligent Systems and Applications, 2014, 03, 6975 Published Online February 2014 in MECS (http://www.mecspress.org/) DOI: 10.5815/ijisa.2014.03.07 Application of GA for Optimal Location of Devices
More informationAn ACO Approach to Solve a Variant of TSP
An ACO Approach to Solve a Variant of TSP Bharat V. Chawda, Nitesh M. Sureja Abstract This study is an investigation on the application of Ant Colony Optimization to a variant of TSP. This paper presents
More informationDistributed Flexible AC Transmission System (D FACTS) Jamie Weber. weber@powerworld.com, 217 384 6330 ext. 13
Distributed Flexible AC Transmission System (D FACTS) Jamie Weber weber@powerworld.com, 217 384 6330 ext. 13 Slide Preparation: Kate Rogers Davis kate@powerworld.com, 217 384 6330, Ext 14 2001 South First
More informationAnalytical review of three latest nature inspired algorithms for scheduling in clouds
International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)  2016 Analytical review of three latest nature inspired algorithms for scheduling in clouds Navneet Kaur Computer
More informationInternational Journal of Software and Web Sciences (IJSWS) www.iasir.net
International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 22790063 ISSN (Online): 22790071 International
More informationInternational Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering
DOI: 10.15662/ijareeie.2014.0307061 Economic Dispatch of Power System Optimization with Power Generation Schedule Using Evolutionary Technique Girish Kumar 1, Rameshwar singh 2 PG Student [Control system],
More informationIMPROVED NETWORK PARAMETER ERROR IDENTIFICATION USING MULTIPLE MEASUREMENT SCANS
IMPROVED NETWORK PARAMETER ERROR IDENTIFICATION USING MULTIPLE MEASUREMENT SCANS Liuxi Zhang and Ali Abur Department of Electrical and Computer Engineering Northeastern University Boston, MA, USA lzhang@ece.neu.edu
More informationCOMPARISON OF THE FACTS EQUIPMENT OPERATION IN TRANSMISSION AND DISTRIBUTION SYSTEMS
COMPARISON OF THE FACTS EQUIPMENT OPERATION IN TRANSMISSION AND DISTRIBUTION SYSTEMS Afshin LASHKAR ARA Azad University of Dezfoul  Iran A_lashkarara@hotmail.com Seyed Ali NABAVI NIAKI University of Mazandaran
More informationA COMPARISON ON PERFORMANCE OF TCSC/SSSC FOR A RADIAL SYSTEM
IMPACT: International Journal of Research in Engineering & Technology (IMPACT: IJRET) ISSN(E): 318843; ISSN(P): 3474599 Vol. 3, Issue 9, Sep 015, 718 Impact Journals A COMPARISON ON PERFORMANCE OF TCSC/SSSC
More informationThe design and performance of Static Var Compensators for particle accelerators
CERNACC20150104 Karsten.Kahle@cern.ch The design and performance of Static Var Compensators for particle accelerators Karsten Kahle, Francisco R. Blánquez, CharlesMathieu Genton CERN, Geneva, Switzerland,
More informationWireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm
, pp. 99108 http://dx.doi.org/10.1457/ijfgcn.015.8.1.11 Wireless Sensor Networks Coverage Optimization based on Improved AFSA Algorithm Wang DaWei and Wang Changliang Zhejiang Industry Polytechnic College
More informationJ. Electrical Systems 112 (2015): 189202. Regular paper
Venkata Padmavathi S 1,*, Sarat Kumar Sahu 2, A Jayalaxmi 3 J. Electrical Systems 112 (2015): 189202 Regular paper Comparison of Hybrid Differential Evolution Algorithm with Genetic Algorithm Based Power
More informationA Binary Model on the Basis of Imperialist Competitive Algorithm in Order to Solve the Problem of Knapsack 10
212 International Conference on System Engineering and Modeling (ICSEM 212) IPCSIT vol. 34 (212) (212) IACSIT Press, Singapore A Binary Model on the Basis of Imperialist Competitive Algorithm in Order
More informationA Fast Computational Genetic Algorithm for Economic Load Dispatch
A Fast Computational Genetic Algorithm for Economic Load Dispatch M.Sailaja Kumari 1, M.Sydulu 2 Email: 1 Sailaja_matam@Yahoo.com 1, 2 Department of Electrical Engineering National Institute of Technology,
More informationOptimal placement of FACTS controllers for maximising system loadability by PSO
Int. J. Power and Energy Conversion, Vol. 4, No. 1, 2013 9 Optimal placement of FACTS controllers for imising system loadability by PSO I. Made Wartana, Jai Govind Singh*, Weerakorn Ongsakul and Sasidharan
More informationPerformance Comparison of GA, DE, PSO and SA Approaches in Enhancement of Total Transfer Capability using FACTS Devices
Journal of Electrical Engineering & Technology Vol. 7, No. 4, pp. 493~500, 2012 493 http://dx.doi.org/10.5370/jeet.2012.7.4.493 Performance Comparison of GA, DE, PSO and SA Approaches in Enhancement of
More informationA Novel Binary Particle Swarm Optimization
Proceedings of the 5th Mediterranean Conference on T33 A Novel Binary Particle Swarm Optimization Motaba Ahmadieh Khanesar, Member, IEEE, Mohammad Teshnehlab and Mahdi Aliyari Shoorehdeli K. N. Toosi
More informationEmail: mod_modaber@yahoo.com. 2Azerbaijan Shahid Madani University. This paper is extracted from the M.Sc. Thesis
Introduce an Optimal Pricing Strategy Using the Parameter of "Contingency Analysis" Neplan Software in the Power Market Case Study (Azerbaijan Electricity Network) ABSTRACT Jalil Modabe 1, Navid Taghizadegan
More informationA RANDOMIZED LOAD BALANCING ALGORITHM IN GRID USING MAX MIN PSO ALGORITHM
International Journal of Research in Computer Science eissn 22498265 Volume 2 Issue 3 (212) pp. 1723 White Globe Publications A RANDOMIZED LOAD BALANCING ALGORITHM IN GRID USING MAX MIN ALGORITHM C.Kalpana
More informationAnalysis of the Effect of Tap Changing Transformer on Performance of SVC
Analysis of the Effect of Tap Changing Transformer on Performance of SVC Dipesh A. Patel 1, Nirav P. Patani 2, Prabhat Kumar 3 1 Senior Lecturer, Electrical Engineering Department, M.L.I.D.S. Bhandu, Gujarat.
More informationCHAPTER 1 INTRODUCTION
CHAPTER 1 INTRODUCTION Power systems form the largest man made complex system. It basically consists of generating sources, transmission network and distribution centers. Secure and economic operation
More informationA New Method for Estimating Maximum Power Transfer and Voltage Stability Margins to Mitigate the Risk of Voltage Collapse
A New Method for Estimating Maximum Power Transfer and Voltage Stability Margins to Mitigate the Risk of Voltage Collapse Bernie Lesieutre Dan Molzahn University of WisconsinMadison PSERC Webinar, October
More informationENHANCEMENT OF POWER SYSTEM SECURITY USING PSONR OPTIMIZATION TECHNIQUE
International Journal of Electrical Engineering & Technology (IJEET) olume 7, Issue 1, JanFeb, 2016, pp.3544, Article ID: IJEET_07_01_004 Available online at http:// http://www.iaeme.com/ijeet/issues.asp?jtype=ijeet&type=7&itype=1
More informationHYBRID ACOIWD OPTIMIZATION ALGORITHM FOR MINIMIZING WEIGHTED FLOWTIME IN CLOUDBASED PARAMETER SWEEP EXPERIMENTS
HYBRID ACOIWD OPTIMIZATION ALGORITHM FOR MINIMIZING WEIGHTED FLOWTIME IN CLOUDBASED PARAMETER SWEEP EXPERIMENTS R. Angel Preethima 1, Margret Johnson 2 1 Student, Computer Science and Engineering, Karunya
More informationReactive Power and Importance to Bulk Power System OAK RIDGE NATIONAL LABORATORY ENGINEERING SCIENCE & TECHNOLOGY DIVISION
Reactive Power and Importance to Bulk Power System OAK RIDGE NATIONAL LABORATORY ENGINEERING SCIENCE & TECHNOLOGY DIVISION Outline What is Reactive Power and where does it come from? Why is it important?
More informationOPTIMAL DISPATCH OF POWER GENERATION SOFTWARE PACKAGE USING MATLAB
OPTIMAL DISPATCH OF POWER GENERATION SOFTWARE PACKAGE USING MATLAB MUHAMAD FIRDAUS BIN RAMLI UNIVERSITI MALAYSIA PAHANG v ABSTRACT In the reality practical power system, power plants are not at the same
More informationBMOA: Binary Magnetic Optimization Algorithm
International Journal of Machine Learning and Computing Vol. 2 No. 3 June 22 BMOA: Binary Magnetic Optimization Algorithm SeyedAli Mirjalili and Siti Zaiton Mohd Hashim Abstract Recently the behavior of
More informationCLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM
CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM *Shabnam Ghasemi 1 and Mohammad Kalantari 2 1 Deparment of Computer Engineering, Islamic Azad University,
More informationAPPLICATION OF ADVANCED SEARCH METHODS FOR AUTOMOTIVE DATABUS SYSTEM SIGNAL INTEGRITY OPTIMIZATION
APPLICATION OF ADVANCED SEARCH METHODS FOR AUTOMOTIVE DATABUS SYSTEM SIGNAL INTEGRITY OPTIMIZATION Harald Günther 1, Stephan Frei 1, Thomas Wenzel, Wolfgang Mickisch 1 Technische Universität Dortmund,
More informationInternational Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net
International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) International Journal of Emerging Technologies in Computational
More informationMétaheuristiques pour l optimisation
Métaheuristiques pour l optimisation Differential Evolution (DE) Particle Swarm Optimization (PSO) Alain Dutech Equipe MAIA  LORIA  INRIA Nancy, France Web : http://maia.loria.fr Mail : Alain.Dutech@loria.fr
More informationCHAPTER 3 SECURITY CONSTRAINED OPTIMAL SHORTTERM HYDROTHERMAL SCHEDULING
60 CHAPTER 3 SECURITY CONSTRAINED OPTIMAL SHORTTERM HYDROTHERMAL SCHEDULING 3.1 INTRODUCTION Optimal shortterm hydrothermal scheduling of power systems aims at determining optimal hydro and thermal generations
More informationDynamic Generation of Test Cases with Metaheuristics
Dynamic Generation of Test Cases with Metaheuristics Laura Lanzarini, Juan Pablo La Battaglia IIILIDI (Institute of Research in Computer Science LIDI) Faculty of Computer Sciences. National University
More informationA Novel Pathway for Portability of Networks and Handingon between Networks
A Novel Pathway for Portability of Networks and Handingon between Networks D. S. Dayana #1, S. R. Surya #2 Department of Computer Applications, SRM University, Chennai, India 1 dayanads@rediffmail.com
More informationOptimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR
International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:5, No:, 20 Optimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR Saeed
More informationA Hybrid Model of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm for Test Case Optimization
A Hybrid Model of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithm for Test Case Optimization Abraham Kiran Joseph a, Dr. G. Radhamani b * a Research Scholar, Dr.G.R Damodaran
More informationTWODIMENSIONAL FINITE ELEMENT ANALYSIS OF FORCED CONVECTION FLOW AND HEAT TRANSFER IN A LAMINAR CHANNEL FLOW
TWODIMENSIONAL FINITE ELEMENT ANALYSIS OF FORCED CONVECTION FLOW AND HEAT TRANSFER IN A LAMINAR CHANNEL FLOW Rajesh Khatri 1, 1 M.Tech Scholar, Department of Mechanical Engineering, S.A.T.I., vidisha
More informationStudy to Determine the Limit of Integrating Intermittent Renewable (wind and solar) Resources onto Pakistan's National Grid
Pakistan Study to Determine the Limit of Integrating Intermittent Renewable (wind and solar) Resources onto Pakistan's National Grid Final Report: Executive Summary  November 2015 for USAID Energy Policy
More informationDr Jamali Publications in English
Dr Jamali Publications in English Journal Papers: [1] A. T. Johns, S. Jamali, Accurate fault location technique for power transmission lines, Proc. IEE, Pt. C, 137, (6), Nov. 1990. [2] S. Jamali, K. Bahari,
More informationA hybrid Approach of Genetic Algorithm and Particle Swarm Technique to Software Test Case Generation
A hybrid Approach of Genetic Algorithm and Particle Swarm Technique to Software Test Case Generation Abhishek Singh Department of Information Technology Amity School of Engineering and Technology Amity
More informationAn ant colony optimization for singlemachine weighted tardiness scheduling with sequencedependent setups
Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, Lisbon, Portugal, September 2224, 2006 19 An ant colony optimization for singlemachine weighted tardiness
More informationComparison of Major Domination Schemes for Diploid Binary Genetic Algorithms in Dynamic Environments
Comparison of Maor Domination Schemes for Diploid Binary Genetic Algorithms in Dynamic Environments A. Sima UYAR and A. Emre HARMANCI Istanbul Technical University Computer Engineering Department Maslak
More informationDesign, Analysis, and Implementation of Solar Power Optimizer for DC Distribution System
Design, Analysis, and Implementation of Solar Power Optimizer for DC Distribution System Thatipamula Venkatesh M.Tech, Power System Control and Automation, Department of Electrical & Electronics Engineering,
More informationHigh Intensify Interleaved Converter for Renewable Energy Resources
High Intensify Interleaved Converter for Renewable Energy Resources K. Muthiah 1, S.Manivel 2, Gowthaman.N 3 1 PG Scholar, Jay Shriram Group of Institutions,Tirupur 2 Assistant Professor, Jay Shriram Group
More informationDually Fed Permanent Magnet Synchronous Generator Condition Monitoring Using Stator Current
Summary Dually Fed Permanent Magnet Synchronous Generator Condition Monitoring Using Stator Current Joachim Härsjö, Massimo Bongiorno and Ola Carlson Chalmers University of Technology Energi och Miljö,
More informationSimulating the Multiple TimePeriod Arrival in Yield Management
Simulating the Multiple TimePeriod Arrival in Yield Management P.K.Suri #1, Rakesh Kumar #2, Pardeep Kumar Mittal #3 #1 Dean(R&D), Chairman & Professor(CSE/IT/MCA), H.C.T.M., Kaithal(Haryana), India #2
More informationA Network Flow Approach in Cloud Computing
1 A Network Flow Approach in Cloud Computing Soheil Feizi, Amy Zhang, Muriel Médard RLE at MIT Abstract In this paper, by using network flow principles, we propose algorithms to address various challenges
More informationA MultiObjective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms
A MultiObjective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms MIGUEL CAMELO, YEZID DONOSO, HAROLD CASTRO Systems and Computer Engineering Department Universidad de los
More informationA Reactive Tabu Search for Service Restoration in Electric Power Distribution Systems
IEEE International Conference on Evolutionary Computation May 411 1998, Anchorage, Alaska A Reactive Tabu Search for Service Restoration in Electric Power Distribution Systems Sakae Toune, Hiroyuki Fudo,
More informationFinding Liveness Errors with ACO
Hong Kong, June 16, 2008 1 / 24 Finding Liveness Errors with ACO Francisco Chicano and Enrique Alba Motivation Motivation Nowadays software is very complex An error in a software system can imply the
More informationDiscrete Hidden Markov Model Training Based on Variable Length Particle Swarm Optimization Algorithm
Discrete Hidden Markov Model Training Based on Variable Length Discrete Hidden Markov Model Training Based on Variable Length 12 Xiaobin Li, 1Jiansheng Qian, 1Zhikai Zhao School of Computer Science and
More informationFlexible Neural Trees Ensemble for Stock Index Modeling
Flexible Neural Trees Ensemble for Stock Index Modeling Yuehui Chen 1, Ju Yang 1, Bo Yang 1 and Ajith Abraham 2 1 School of Information Science and Engineering Jinan University, Jinan 250022, P.R.China
More informationParallelized Cuckoo Search Algorithm for Unconstrained Optimization
Parallelized Cuckoo Search Algorithm for Unconstrained Optimization Milos SUBOTIC 1, Milan TUBA 2, Nebojsa BACANIN 3, Dana SIMIAN 4 1,2,3 Faculty of Computer Science 4 Department of Computer Science University
More informationTechnical Analysis on Financial Forecasting
Technical Analysis on Financial Forecasting SGopal Krishna Patro 1, Pragyan Parimita Sahoo 2, Ipsita Panda 3, Kishore Kumar Sahu 4 1,2,3,4 Department of CSE & IT, VSSUT, Burla, Odisha, India sgkpatro2008@gmailcom,
More informationA progressive method to solve largescale AC Optimal Power Flow with discrete variables and control of the feasibility
A progressive method to solve largescale AC Optimal Power Flow with discrete variables and control of the feasibility Manuel Ruiz, Jean Maeght, Alexandre Marié, Patrick Panciatici and Arnaud Renaud manuel.ruiz@artelys.com
More informationLAB1 INTRODUCTION TO PSS/E EE 461 Power Systems Colorado State University
LAB1 INTRODUCTION TO PSS/E EE 461 Power Systems Colorado State University PURPOSE: The purpose of this lab is to introduce PSS/E. This lab will introduce the following aspects of PSS/E: Introduction to
More informationSPEED CONTROL OF INDUCTION MACHINE WITH REDUCTION IN TORQUE RIPPLE USING ROBUST SPACEVECTOR MODULATION DTC SCHEME
International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 7, Issue 2, MarchApril 2016, pp. 78 90, Article ID: IJARET_07_02_008 Available online at http://www.iaeme.com/ijaret/issues.asp?jtype=ijaret&vtype=7&itype=2
More informationA Robust Method for Solving Transcendental Equations
www.ijcsi.org 413 A Robust Method for Solving Transcendental Equations Md. Golam Moazzam, Amita Chakraborty and Md. AlAmin Bhuiyan Department of Computer Science and Engineering, Jahangirnagar University,
More informationA New Natureinspired Algorithm for Load Balancing
A New Natureinspired Algorithm for Load Balancing Xiang Feng East China University of Science and Technology Shanghai, China 200237 Email: xfeng{@ecusteducn, @cshkuhk} Francis CM Lau The University of
More informationOptimization of PID parameters with an improved simplex PSO
Li et al. Journal of Inequalities and Applications (2015) 2015:325 DOI 10.1186/s1366001507852 R E S E A R C H Open Access Optimization of PID parameters with an improved simplex PSO Jimin Li 1, YeongCheng
More informationNAIS: A Calibrated Immune Inspired Algorithm to solve Binary Constraint Satisfaction Problems
NAIS: A Calibrated Immune Inspired Algorithm to solve Binary Constraint Satisfaction Problems Marcos Zuñiga 1, MaríaCristina Riff 2 and Elizabeth Montero 2 1 Projet ORION, INRIA SophiaAntipolis Nice,
More informationResearch on the Performance Optimization of Hadoop in Big Data Environment
Vol.8, No.5 (015), pp.93304 http://dx.doi.org/10.1457/idta.015.8.5.6 Research on the Performance Optimization of Hadoop in Big Data Environment Jia MinZheng Department of Information Engineering, Beiing
More informationANT COLONY OPTIMIZATION ALGORITHM FOR RESOURCE LEVELING PROBLEM OF CONSTRUCTION PROJECT
ANT COLONY OPTIMIZATION ALGORITHM FOR RESOURCE LEVELING PROBLEM OF CONSTRUCTION PROJECT Ying XIONG 1, Ya Ping KUANG 2 1. School of Economics and Management, Being Jiaotong Univ., Being, China. 2. College
More informationHeuristicBased Firefly Algorithm for Bound Constrained Nonlinear Binary Optimization
HeuristicBased Firefly Algorithm for Bound Constrained Nonlinear Binary Optimization M. Fernanda P. Costa 1, Ana Maria A.C. Rocha 2, Rogério B. Francisco 1 and Edite M.G.P. Fernandes 2 1 Department of
More informationFACTS. Solutions to optimise network performance GRID
Solutions to optimise network performance GRID Solutions to optimise your network Our worldwide presence: Better solutions for your network all around the world Tampere Philadelphia Stafford Konstanz Beijing
More informationDynamic Load Balancing of Virtual Machines using QEMUKVM
Dynamic Load Balancing of Virtual Machines using QEMUKVM Akshay Chandak Krishnakant Jaju Technology, College of Engineering, Pune. Maharashtra, India. Akshay Kanfade Pushkar Lohiya Technology, College
More informationHYBRID GENETIC ALGORITHM PARAMETER EFFECTS FOR OPTIMIZATION OF CONSTRUCTION RESOURCE ALLOCATION PROBLEM. JinLee KIM 1, M. ASCE
1560 HYBRID GENETIC ALGORITHM PARAMETER EFFECTS FOR OPTIMIZATION OF CONSTRUCTION RESOURCE ALLOCATION PROBLEM JinLee KIM 1, M. ASCE 1 Assistant Professor, Department of Civil Engineering and Construction
More informationOPTIMAL DG PLACEMENT BASED ON STATIC SECURITY ASSESSMENT
International Journal of Electrical Engineering & Technology (IJEET) Volume 7, Issue 2, MarchApril, 2016, pp.37 49, Article ID: IJEET_07_02_005 Available online at http:// http://www.iaeme.com/ijeet/issues.asp?jtype=ijeet&vtype=7&itype=2
More informationAPPLICATION OF MODIFIED (PSO) AND SIMULATED ANNEALING ALGORITHM (SAA) IN ECONOMIC LOAD DISPATCH PROBLEM OF THERMAL GENERATING UNIT
International Journal of Electrical Engineering & Technology (IJEET) Volume 7, Issue 2, MarchApril, 2016, pp.69 78, Article ID: IJEET_07_02_008 Available online at http:// http://www.iaeme.com/ijeet/issues.asp?jtype=ijeet&vtype=7&itype=2
More informationA MULTILEVEL INVERTER FOR SYNCHRONIZING THE GRID WITH RENEWABLE ENERGY SOURCES BY IMPLEMENTING BATTERY CUM DCDC CONERTER
A MULTILEVEL INVERTER FOR SYNCHRONIZING THE GRID WITH RENEWABLE ENERGY SOURCES BY IMPLEMENTING BATTERY CUM DCDC CONERTER 1 KARUNYA CHRISTOBAL LYDIA. S, 2 SHANMUGASUNDARI. A, 3 ANANDHI.Y 1,2,3 Electrical
More informationHybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling
Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling R.G. Babukartik 1, P. Dhavachelvan 1 1 Department of Computer Science, Pondicherry University, Pondicherry, India {r.g.babukarthik,
More informationUsing Data Mining for Mobile Communication Clustering and Characterization
Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer
More informationGenetic Algorithms for MultiObjective Optimization in Dynamic Systems
Genetic Algorithms for MultiObjective Optimization in Dynamic Systems Ceyhun Eksin Boaziçi University Department of Industrial Engineering Boaziçi University, Bebek 34342, stanbul, Turkey ceyhun.eksin@boun.edu.tr
More informationAdaptive Equalization of binary encoded signals Using LMS Algorithm
SSRG International Journal of Electronics and Communication Engineering (SSRGIJECE) volume issue7 Sep Adaptive Equalization of binary encoded signals Using LMS Algorithm Dr.K.Nagi Reddy Professor of ECE,NBKR
More informationGenetic Algorithm approach to find excitation capacitances for 3 phase smseig operating single phase loads
Genetic Algorithm approach to find excitation capacitances for 3 phase smseig operating single phase loads Authors & Affiliation: V.Ravi Kiran, V.Manoj and P.Praveen Kumar Asst. Professor, EEE Dept GMRIT,
More informationThree phase circuits
Three phase circuits THREE PHASE CIRCUITS THREEPHASE ADVANTAGES 1. The horsepower rating of threephase motors and the kva rating of threephase transformers are 150% greater than singlephase motors
More informationA Novel Web Optimization Technique using Enhanced Particle Swarm Optimization
A Novel Web Optimization Technique using Enhanced Particle Swarm Optimization P.N.Nesarajan Research Scholar, Erode Arts & Science College, Erode M.Venkatachalam, Ph.D Associate Professor & HOD of Electronics,
More informationResource Provisioning in Single Tier and MultiTier Cloud Computing: StateoftheArt
Resource Provisioning in Single Tier and MultiTier Cloud Computing: StateoftheArt Marwah Hashim Eawna Faculty of Computer and Information Sciences Salma Hamdy Mohammed Faculty of Computer and Information
More informationOptimal Power Flow Analysis of Energy Storage for Congestion Relief, Emissions Reduction, and Cost Savings
1 Optimal Power Flow Analysis of Energy Storage for Congestion Relief, Emissions Reduction, and Cost Savings Zhouxing Hu, Student Member, IEEE, and Ward T. Jewell, Fellow, IEEE Abstract AC optimal power
More informationProduction Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization
American J. of Engineering and Applied Sciences 3 (1): 714, 2010 ISSN 19417020 2010 Science Publications Production Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization
More informationOptimal Feeder Reconfiguration with Distributed Generation in ThreePhase Distribution System by Fuzzy Multiobjective and Tabu Search
Optimal Feeder Reconfiguration with Distributed Generation in ThreePhase Distribution System by Fuzzy Multiobjective and Tabu Search Nattachote Rugthaicharoencheep and Somporn Sirisumranukul King Mongkut
More informationINVESTIGATION OF ELECTRIC FIELD INTENSITY AND DEGREE OF UNIFORMITY BETWEEN ELECTRODES UNDER HIGH VOLTAGE BY CHARGE SIMULATIO METHOD
INVESTIGATION OF ELECTRIC FIELD INTENSITY AND DEGREE OF UNIFORMITY BETWEEN ELECTRODES UNDER HIGH VOLTAGE BY CHARGE SIMULATIO METHOD Md. Ahsan Habib, Muhammad Abdul Goffar Khan, Md. Khaled Hossain, Shafaet
More informationJournal of Theoretical and Applied Information Technology 20 th July 2015. Vol.77. No.2 20052015 JATIT & LLS. All rights reserved.
EFFICIENT LOAD BALANCING USING ANT COLONY OPTIMIZATION MOHAMMAD H. NADIMISHAHRAKI, ELNAZ SHAFIGH FARD, FARAMARZ SAFI Department of Computer Engineering, Najafabad branch, Islamic Azad University, Najafabad,
More informationA Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem
A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem Sayedmohammadreza Vaghefinezhad 1, Kuan Yew Wong 2 1 Department of Manufacturing & Industrial Engineering, Faculty of Mechanical
More informationAN EFFICIENT DISTRIBUTED CONTROL LAW FOR LOAD BALANCING IN CONTENT DELIVERY NETWORKS
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 9, September 2014,
More informationHighMix LowVolume Flow Shop Manufacturing System Scheduling
Proceedings of the 14th IAC Symposium on Information Control Problems in Manufacturing, May 2325, 2012 HighMix LowVolume low Shop Manufacturing System Scheduling Juraj Svancara, Zdenka Kralova Institute
More informationOptimization of Preventive Maintenance Scheduling in Processing Plants
18 th European Symposium on Computer Aided Process Engineering ESCAPE 18 Bertrand Braunschweig and Xavier Joulia (Editors) 2008 Elsevier B.V./Ltd. All rights reserved. Optimization of Preventive Maintenance
More informationA New Quantitative Behavioral Model for Financial Prediction
2011 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (2011) (2011) IACSIT Press, Singapore A New Quantitative Behavioral Model for Financial Prediction Thimmaraya Ramesh
More informationA Service Revenueoriented Task Scheduling Model of Cloud Computing
Journal of Information & Computational Science 10:10 (2013) 3153 3161 July 1, 2013 Available at http://www.joics.com A Service Revenueoriented Task Scheduling Model of Cloud Computing Jianguang Deng a,b,,
More informationSOFTWARE FOR GENERATION OF SPECTRUM COMPATIBLE TIME HISTORY
3 th World Conference on Earthquake Engineering Vancouver, B.C., Canada August 6, 24 Paper No. 296 SOFTWARE FOR GENERATION OF SPECTRUM COMPATIBLE TIME HISTORY ASHOK KUMAR SUMMARY One of the important
More informationThe Use of Cuckoo Search in Estimating the Parameters of Software Reliability Growth Models
The Use of Cuckoo Search in Estimating the Parameters of Software Reliability Growth Models Dr. Najla Akram ALSaati and Marwa AbdALKareem Software Engineering Dept. College of Computer Sciences & Mathematics,
More informationLearning in Abstract Memory Schemes for Dynamic Optimization
Fourth International Conference on Natural Computation Learning in Abstract Memory Schemes for Dynamic Optimization Hendrik Richter HTWK Leipzig, Fachbereich Elektrotechnik und Informationstechnik, Institut
More informationProgramming Risk Assessment Models for Online Security Evaluation Systems
Programming Risk Assessment Models for Online Security Evaluation Systems Ajith Abraham 1, Crina Grosan 12, Vaclav Snasel 13 1 Machine Intelligence Research Labs, MIR Labs, http://www.mirlabs.org 2 BabesBolyai
More informationModelbased Parameter Optimization of an Engine Control Unit using Genetic Algorithms
Symposium on Automotive/Avionics Avionics Systems Engineering (SAASE) 2009, UC San Diego Modelbased Parameter Optimization of an Engine Control Unit using Genetic Algorithms Dipl.Inform. Malte Lochau
More informationSOFTWARE FOR THE OPTIMAL ALLOCATION OF EV CHARGERS INTO THE POWER DISTRIBUTION GRID
SOFTWARE FOR THE OPTIMAL ALLOCATION OF EV CHARGERS INTO THE POWER DISTRIBUTION GRID Amparo MOCHOLÍ MUNERA, Carlos BLASCO LLOPIS, Irene AGUADO CORTEZÓN, Vicente FUSTER ROIG Instituto Tecnológico de la Energía
More informationPerformance Evaluation of Task Scheduling in Cloud Environment Using Soft Computing Algorithms
387 Performance Evaluation of Task Scheduling in Cloud Environment Using Soft Computing Algorithms 1 R. Jemina Priyadarsini, 2 Dr. L. Arockiam 1 Department of Computer science, St. Joseph s College, Trichirapalli,
More informationANN Based Fault Classifier and Fault Locator for Double Circuit Transmission Line
International Journal of Computer Sciences and Engineering Open Access Research Paper Volume4, Special Issue2, April 2016 EISSN: 23472693 ANN Based Fault Classifier and Fault Locator for Double Circuit
More informationEA and ACO Algorithms Applied to Optimizing Location of Controllers in Wireless Networks
2 EA and ACO Algorithms Applied to Optimizing Location of Controllers in Wireless Networks DacNhuong Le, Hanoi University of Science, Vietnam National University, Vietnam Optimizing location of controllers
More informationInternational Conference on Advances in Energy, Environment and Chemical Engineering (AEECE2015)
International Conference on Advances in Energy, Environment and Chemical Engineering (AEECE2015) An Optimal Reactive Power Planning Software for Urban Network Qiang Sun 1, a, Xue Wang 1,b, Yingting Ni
More information2004 Networks UK Publishers. Reprinted with permission.
Riikka Susitaival and Samuli Aalto. Adaptive load balancing with OSPF. In Proceedings of the Second International Working Conference on Performance Modelling and Evaluation of Heterogeneous Networks (HET
More information