World Academy of Scence, Engneerng and Technology Internatonal Journal of Electrcal and Computer Engneerng ol:8, No:, 014 Network Reconfguraton of Dstrbuton System Usng Artfcal Bee Colony Algorthm S. Ganesh Internatonal Scence Index, Electrcal and Computer Engneerng ol:8, No:, 014 waset.org/publcaton/9997973 Abstract Power dstrbuton systems typcally have te and sectonalzng swtches whose states determne the topologcal confguraton of the network. The am of network reconfguraton of the dstrbuton network s to mnmze the losses for a load arrangement at a partcular tme. Thus the objectve functon s to mnmze the losses of the network by satsfyng the dstrbuton network constrants. The varous constrants are radalty, voltage lmts and the power balance condton. In ths paper the status of the swtches s obtaned by usng Artfcal Bee Colony (ABC) algorthm. ABC s based on a partcular ntellgent behavor of honeybee swarms. ABC s developed based on nspectng the behavors of real bees to fnd nectar and sharng the nformaton of food sources to the bees n the hve. The proposed methodology has three stages. In stage one ABC s used to fnd the te swtches, n stage two the dentfed te swtches are checked for radalty constrant and f the radlaty constrant s satsfed then the procedure s proceeded to stage three otherwse the process s repeated. In stage three load flow analyss s performed. The process s repeated tll the losses are mnmzed. The ABC s mplemented to fnd the power flow path and the Forward Sweeper algorthm s used to calculate the power flow parameters. The proposed methodology s appled for a 33 bus sngle feeder dstrbuton network usng MATLAB. Keywords Artfcal Bee Colony (ABC) algorthm, Dstrbuton system, Loss reducton, Network reconfguraton. I. INTRODUCTION HE electrc utlty system s usually dvded nto three T subsystems whch are generaton, transmsson, and dstrbuton. A fourth dvson, whch sometmes made, s called sub transmsson. However, sub transmsson can really be consdered as a subset of transmsson snce the voltage levels and protecton practces are qute smlar. The dstrbuton system s commonly broken down nto three components: dstrbuton substaton, dstrbuton prmary and secondary. At the substaton level, the voltage s reduced and the power s dstrbuted n smaller amounts to the customers. Consequently, one substaton wll supply many customers wth power. Thus, the number of transmsson lnes n the dstrbuton systems s many tmes that of the transmsson systems. Furthermore, most customers are connected to only one of the three phases n the dstrbuton system. Therefore, the power flow on each of the lnes s dfferent and the system s typcally unbalanced. Ths characterstc needs to be accounted for n load flow studes related to dstrbuton networks. S. Ganesh s wth the Department of Electrcal and Electroncs Engneerng, Chandy College of Engneerng, Thoothukud, Anna Unversty, Chenna, Taml Nadu, Inda (e mal: gaut.ganeshs@gmal.com). Electrc power dstrbuton s the porton of the power delvery nfrastructure that takes the electrcty from the hghly meshed, hgh-voltage transmsson crcuts and delverng t to customers. Prmary dstrbuton lnes are medum-voltage crcuts, normally thought of as 600 to 35k. At a dstrbuton substaton, a substaton transformer takes the ncomng transmsson- level voltage (35 to 30k) and steps t down to several dstrbuton prmary crcuts, whch gets regulated from the substaton. Close to each end user, a dstrbuton transformer takes the prmary-dstrbuton voltage and steps t down to a low-voltage secondary crcut (commonly 10/40; other utlzaton voltages are used as well). From the dstrbuton transformer, the secondary dstrbuton crcuts connect to the end user where the connecton s made at the servce entrance. Functonally, dstrbuton crcuts are those that feed customers. The dstrbuton system s fed through dstrbuton substatons. These substatons have an almost nfnte number of desgns based on consderaton such as load densty, hgh sde and low sde voltage, land avalablty, relablty requrements, load growth, voltage drop, cost and losses, etc. In practce, the followng dstrbuton crcuts are generally used. Accordng to connecton scheme the dstrbuton system has two types as gven below:. Radal System.. Rng Man System. A radal system has only one power source for a group of customers. A power falure, short crcut, or a downed power lne would nterrupt power n the entre lne whch must be fxed before power can be restored. Radal dstrbuton system s wdely used because t s cheap and has easy mantenance. II. FEEDER RECONFIGURATION In a dstrbuton system, each feeder has a dfferent mxture of commercal, resdental, and ndustral type loads. These load types have dfferent daly patterns whch make the peak load of feeders occur at dfferent tmes. In normal operatng condtons, part of loads can be transferred from heavly loaded to relatvely less heavly loaded feeders by network reconfguraton. Dstrbuton feeders contan number of swtches that are normally closed (sectonalzed swtches) and swtches that are normally open (te swtches). Dstrbuton network reconfguraton s the process of alterng the topologcal structure of dstrbuton network by closng the open/ close status of sectonalzng and te swtches. When the operaton condtons change, network reconfguraton s performed by the openng/closng of the network swtches under the Internatonal Scholarly and Scentfc Research & Innovaton 8() 014 396
World Academy of Scence, Engneerng and Technology Internatonal Journal of Electrcal and Computer Engneerng ol:8, No:, 014 Internatonal Scence Index, Electrcal and Computer Engneerng ol:8, No:, 014 waset.org/publcaton/9997973 constrants of transformer capacty, feeder thermal capacty, voltage drop and radalty of the network. The dstrbuton network s reconfgured for the followng objectves: 1. Reducng power losses.. Relevng overload (balance loadng). 3. Reducng voltage devatons. 4. Restorng the system One mportant area n whch dstrbuton automaton s beng appled s the area of network reconfguraton. Network reconfguraton refers to the closng and openng of swtches n a power dstrbuton system n order to alter the network topology, enablng the flow of power from the substaton to the customers. There are two prmary reasons to reconfgure a dstrbuton network durng normal operaton. 1) Dependng on the current loadng condtons, reconfguraton may become necessary n order to elmnate overloads on specfc system components such as transformers or lne sectons. In ths case t s known as load balancng. ) As the loadng condtons on the system change t may also become proftable to reconfgure n order to reduce the real power losses n the network. Ths s usually referred to as network reconfguraton for loss reducton. Network reconfguraton n both of the above cases can be classfed as a mnmal spannng tree problem, whch s named as NP complete combnatoral optmzaton problem. A method s needed to fnd the network confguraton quckly whch mnmzes the total real power loss of the network whle satsfyng all system constrants. Several approaches have been appled to the soluton of ths problem wth varyng degrees of success. Dstrbuton system reconfguraton for loss reducton was frst proposed by Merln and Back [1]. They employed a blend of optmzaton and heurstcs to determne the mnmal loss operatng confguraton for the dstrbuton system represented by a spannng tree structure at a specfc load condton. The strength of the algorthm s that an optmal soluton can be obtaned whch s ndependent of the ntal swtch status. But the shortcomngs n the paper are: 1) contrbuton of only real component of current was consdered whle calculatng power loss and assumed that the voltage angles are neglgble; ) the losses assocated wth lne equpment are not consdered; and 3) the soluton proved to be very tme consumng as the possble system confguratons are, where s lne sectons equpped wth swtches. Baran and Wu [] presented a heurstc reconfguraton methodology based on the branch exchange to reduce losses and balance the loads n the feeders. To assst n the search, two approxmated load flows for radal networks wth dfferent degrees of accuracy are used. They are smple Dst flow method and back and forward update of Dst flow method. Ths method s very tme consumng due to the complcated combnatons n large scale system and converges to a local optmum soluton, because convergence to the global optmum s not guaranteed. Safr and Chkhan [3] defned a new set of heurstc rules for dstrbuton system reconfguraton problem. The rules have been developed wth the objectve of reducng losses drectly and make an effort to quantze the sutablty of swtchng optons. The proposed method serves as a preprocessor to a reconfguraton algorthm removng undesrable swtchng optons wthout the need to perform a complex load flow analyss. Chang and Darlng [4] proposed an effcent algorthm for real network reconfguraton on large unbalanced dstrbuton networks where the am s to change the network topology as needed for loss reducton and load balancng n response to system and load varatons. Zhou [5] refned the GA method by modfyng the strng structure and used approxmated ftness functon whch leads drectly to unrelable solutons. Some mprovements are made on chromosome codng (real codng), ftness functon and mutaton patterns. Among these mproved features, an adaptve process of mutaton s developed not only to present premature convergence but also to produce smooth convergence. Ln and Cheng [6] proposed a Refned GA (RGA) that takes advantage of the optmal flow pattern, GA and the Tabu Search (TS). Cross over and mutatons were combned n RGA. Parsad and Ranjan [7] proposed a fuzzy mutated GA whch overcomes the combnaton nature of the reconfguraton problem and deals wth non contnuous mult objectves optmzaton. The attractve features of the algorthm are: presentaton of radal property n the network wthout solatng any load ponts by an elegant codng scheme and an effcent convergence characterstcs attrbuted to a controlled mutaton usng fuzzy logc. Km [8] presented the strategy of feeder reconfguraton to reduce the power loss by Artfcal Neural (ANN) Network. In ths approach the load transfer and the correspondng load flow soluton durng the search process are not requred. The tranng set of ANN s the optmal system topology correspondng to varous load patterns whch mnmzes the load under gven condtons. Ln [9] presented a rule based expert system wth a Colored Pert Net (CPN) algorthm for load balancng of dstrbuton system. CPN models of the dstrbuton components such as four ways lne swtches are proposed to derve the proper swtchng operaton. L and Chen [10] ntroduced an effcent and robust method based on Tabu Search (TS) technque whch s a recent member n the famly of modern heurstcs methods to solve the problem of network reconfguraton n dstrbuton system to reduce the lne losses under normal operatng condtons. TS s a heurstc optmzaton technque whch obtans the optmal soluton of combnatoral optmzaton problem. Based on the above lterature survey, the applcaton of graph theory to address the PSR problem was dentfed as a potental research area. Further, confnng the scope of hybrd algorthm s ncreasng. The network reconfguraton problem n a dstrbuton system s to fnd a best confguraton of radal network that ncurs mnmum power loss whle the mposed operatng constrants are satsfed. So, n ths paper Artfcal Internatonal Scholarly and Scentfc Research & Innovaton 8() 014 397
World Academy of Scence, Engneerng and Technology Internatonal Journal of Electrcal and Computer Engneerng ol:8, No:, 014 Internatonal Scence Index, Electrcal and Computer Engneerng ol:8, No:, 014 waset.org/publcaton/9997973 Bee Colony (ABC) s proposed for the mnmzaton of power loss n the dstrbuton system. The am of ths paper s to fnd the optmal power flow paths by usng the ABC method. The role of ABC s to generate a sequence of swtchng combnatons for fndng the power flow path. Feeder reconfguraton problem belongs to a category of optmzaton and graph theory problems known as Spannng Tree. It means havng nodes of a graph; branches of that graph are determned so that a well defned objectve functon s optmzed. It can also be used to satsfy the constrants. The operatng constrants consdered are voltage profle of the system, and radal structure of the dstrbuton system. So, to check the voltage profle constrant load flow analyss s used. Spannng tree concept s used for radalty constrant. The proposed method s tested on 33 bus system and the results are obtaned usng MATLAB. III. FORMULATION OF THE PROBLEM The network reconfguraton problem n a dstrbuton system s to fnd a best confguraton of radal network that gves optmum power paths whle the mposed operatng constrants are satsfed. The constrants are () voltage constrants and () radalty constrants. Ths s a combnatoral problem snce the soluton nvolves the consderaton of all possble spannng trees. The objectve functon s to mnmze the power loss. The constrants are; 1. Radalty. oltage Lmts < mn < max where : oltage at recevng end of the branch mn : Mnmum oltage at recevng end of the branch max : Maxmum oltage at recevng end of the branch The network reconfguraton has to obey the followng rules: 1) No feeder secton can be left out of servce ) Radal network structure must be retaned. Power flow n a radal dstrbuton network can be descrbed by a set of recursve equatons called dstrbuton flow branch equatons that use the real power, reactve power and voltage. Fg. 1 shows the sngle lne dagram of radal dstrbuton network. (P + Q ) P = P r P + 1 L(+ 1) ( P + Q ) Q 1 = + Q x Q L( + 1) (1) () (3) Fg. 1 One lne dagram of a radal network (r + x )(P + Q ) = (r P + x Q ) + + 1 The power loss n a branch s expressed as r (P + Q ) LP = where LP Power loss n the th branch N Number of buses r Resstance of the branch P Real power flowng through the branch Q Reactve power flowng through the branch I. ARTIFICIAL BEE COLONY (ABC) Karaboga developed a new optmzaton algorthm called the ABC algorthm [11]. The ABC algorthm was frst ntroduced for numercal optmzaton problems based on the foragng behavor of a honey bee swarm. Further mprovements of the ABC algorthm have been carred out by [1]. ABC s developed based on nspectng the behavors of real bees to fnd nectar and sharng the nformaton of food sources to the bees n the hve. Agents n ABC The Employed Bee The Onlooker Bee The Scout The Employed Bee: It stays on a food source and provdes the neghborhood of the source n ts memory. The Onlooker Bee: It gets the nformaton of food sources from the employed bees n the hve and select one of the food source to gathers the nectar. The Scout: It s responsble for fndng new food, the new nectar, and sources. The colony of artfcal bees conssts of three groups of bees: employed bees, onlookers and scouts. The frst half of (4) (5) Internatonal Scholarly and Scentfc Research & Innovaton 8() 014 398
World Academy of Scence, Engneerng and Technology Internatonal Journal of Electrcal and Computer Engneerng ol:8, No:, 014 Internatonal Scence Index, Electrcal and Computer Engneerng ol:8, No:, 014 waset.org/publcaton/9997973 the colony conssts of the employed artfcal bees and the second half ncludes the onlookers. For every food source, there s only one employed bee. In other words, the number of employed bees s equal to the number of food sources around the hve. The employed bee whose food source has been exhausted by the bees becomes a scout. Each cycle of the search conssts of three steps: movng the employed and onlooker bees onto the food sources, calculatng ther nectar amounts and determnng the scout bees and drectng them onto possble food sources. A food source poston represents a possble soluton to the problem to be optmzed. The amount of nectar of a food source corresponds to the qualty of the soluton represented by that food source [13]. Onlookers are placed on the food sources by usng a probablty based selecton process. As the nectar amount of a food source ncreases, the probablty value wth whch the food source s preferred by onlookers ncreases, too. [13] Every bee colony has scouts that are the colony s explorers. The explorers do not have any gudance whle lookng for food. They are prmarly concerned wth fndng any knd of food source. As a result of such behavor, the scouts are characterzed by low search costs and a low average n food source qualty. Occasonally, the scouts can accdentally dscover rch, entrely unknown food sources. In the case of artfcal bees, the artfcal scouts could have the fast dscovery of the group of feasble soluton. In ths work, one of the employed bees s selected and classfed as the scout bee. The selecton s controlled by a control parameter called "lmt". If a soluton representng a food source s not mproved by a predetermned number of trals, then that food source s abandoned by ts employed bee and the employed bee s converted to a scout. The number of trals for releasng a food source s equal to the value of "lmt" whch s an mportant control parameter of ABC [13]. In a robust search process exploraton and explotaton processes must be carred out together. In the ABC algorthm, whle onlookers and employed bees carry out the explotaton process n the search space, the scouts control the exploraton process. In the case of real honey bees, the recrutment rate represents a measure of how quckly the bee swarm locates and explots the newly dscovered food source. Artfcal recrutng process could smlarly represent the measurement of the speed wth whch the feasble solutons or the optmal solutons of the dffcult optmzaton problems can be dscovered. The survval and progress of the real bee swarm depends upon the rapd dscovery and effcent utlzaton of the best food resources [13]. Smlarly the optmal soluton for dffcult engneerng problems s connected to the relatvely fast dscovery of good solutons especally for the problems that need to be solved n short tme. A. ABC Procedure Step1. Intalze the populaton. Step. Modfy postons. Step3. Apply selecton crteron. Step4. Repeat (cycle). Step5. Allow the employed bees to share the food nformaton wth onlooker bees. Step6. Allow the onlooker bees to choose the best food source based on the probablty calculaton. Step7. Apply selecton crteron. Step8. Check for an abundant soluton, any (f exsts) ntate a new food-source poston. Otherwse, follow the next step. Step9. Retan best soluton so far. Step10. Untl the results are obtaned.. PROPOSED METHODOLOGY In the proposed methodology, parameters are ntalzed and a New Bee Colony populaton s generated. The colony populaton s flled wth many randomly generated soluton vectors. Then the radalty constrants are checked, f the radalty constrants are satsfed then the load flow analyss s performed and voltage constrants are checked. If the number of teratons s volated then the optmal swtchng sequence s dsplayed, otherwse a new populaton s mprovsed based on the worst case, the colony populaton s updated and the stoppng crteron are checked. If stoppng crteron s acheved then dsplay the optmal swtchng sequence. The followng steps are nvolved n the proposed methodology The followng steps are nvolved n the proposed methodology. Step1. Start Step. Generate a new Bee Colony populaton Step3. Check for radalty constrants usng Brute Force technque Step4. If satsfed go to step 5 otherwse go to step Step5. Perform load flow analyss Step6. Check for voltage lmts Step7. If no of teratons s over, go to step 9 otherwse go to step 8 Step8. Improvse a new populaton usng The Employed Bee, The Onlooker Bee, The Scout, Update the colony populaton and Check the stoppng crteron Step9. Dsplay the optmal swtchng sequence I.TEST PROBLEM The proposed Artfcal Bee Colony (ABC) s appled to 33 bus network. The resultant power flow path s shown along wth new te swtches. The results are also compared wth exstng method. The proposed method s also used to fnd optmal power flow path. To demonstrate the effcency of the ABC, the proposed method s tested on the 33 bus system. The detals of the 33 bus system are gven below: Number of buses 33 Number of branches 37 Number of te lnes 5 Te lnes S33, S34, S35, S36, S37 Total real power 355 MW Total reactve power 300 MAR Internatonal Scholarly and Scentfc Research & Innovaton 8() 014 399
World Academy of Scence, Engneerng and Technology Internatonal Journal of Electrcal and Computer Engneerng ol:8, No:, 014 The proposed method s tested on the 33 bus system. A sngle lne dagram of a 33 bus system s shown n Fg.. The dstrbuton network of a 33 bus system has two types of swtches, dark lnes are sectonalzng swtches whch are normally closed and dot lnes are te swtches whch are normally open. TABLE II PERFORMANCE OF THE PROPOSED METHODOLOGY Maxmum total Mnmum total Average total Algorthm loss (KW) loss (KW) loss (KW) ABC 1047.4 134.6 375.01 The ABC s executed four tmes and the best result s obtaned for the swtchng sequence havng the te swtches, whch S4, S10, S19, S5, S35 are as follows. The ABC Result for 33 bus system s shown n Fg. 3. Internatonal Scence Index, Electrcal and Computer Engneerng ol:8, No:, 014 waset.org/publcaton/9997973 Fg. 33 Bus test Network TABLE I ABC RESULT FOR 33 BUS SYSTEM Te swtches Total real power loss Mnmum kw p.u. voltage S S5 S1 S15 S35 404.45 0.957 S6 S9 S1 S7 S37 6.61 0.964 S7 S10 S1 S0 S3 390.68 0.9789 S4 S11 S1 S16 S19 377.5 0.978 S S8 S13 S6 S33 416.91 0.9816 S4 S9 S1 S4 S9 35.06 0.9563 S5 S13 S S9 S35 56.61 0.977 S7 S9 S14 S6 S37 379.93 0.9801 S S5 S9 S1 S15 1.07 0.9861 S10 S1 S0 S3 S34 730.5 0.9641 S4 S14 S6 S33 S35 1047.0 0.9371 S7 S1 S6 S3 S35 77.38 0.9771 S5 S11 S3 S3 S34 14.9 0.9783 S4 S9 S14 S4 S9 169.83 0.9748 S4 S10 S19 S5 S35 134.6 0.9853 The results obtaned durng varous tme of executon are tabulated n Table I. The node voltage of 33 bus system s compared before and after reconfguraton. Before reconfguraton the lowest bus bar voltage s 0.905p.u, whch occurs at node 18. After reconfguraton, the mnmum node voltage of the system has mproved to 0.9853p.u, whch occurs at node 33. The performance of the proposed methodology and node voltages are shown n Tables II and III. Bus oltage 1 0.98 0.96 0.94 0.9 Fg. 3 ABC Result for 33 bus system Bus voltage S Bus number Before Reconfguraton After Reconfguraton usng ABC 0.9 0 5 10 15 0 5 30 35 Bus Number Fg. 4 oltage profle for 33 bus system The voltage profles of the system before and after reconfguraton are shown n Fg. 4. The mnmum voltage n the system after reconfguraton s mproved by 8.1%. Internatonal Scholarly and Scentfc Research & Innovaton 8() 014 400
World Academy of Scence, Engneerng and Technology Internatonal Journal of Electrcal and Computer Engneerng ol:8, No:, 014 Internatonal Scence Index, Electrcal and Computer Engneerng ol:8, No:, 014 waset.org/publcaton/9997973 TABLE III NODE OLTAGE OF 33 BUS SYSTEM Bus No Before Reconfguraton After Reconfguraton Usng ABC oltage oltage 1 1.0000 1.0000 0.9970 0.9995 S3 0.989 0.9978 S4 0.9754 0.9975 S5 0.9680 1.0000 S6 0.9496 0.9978 S7 0.9461 0.997 S8 0.934 0.9964 S9 0.961 0.9944 S10 0.903 0.9901 S11 0.9194 1.0000 S1 0.9179 0.9991 S13 0.9131 0.9953 S14 0.9110 0.9938 S15 0.9095 0.991 S16 0.9080 0.993 S17 0.9058 0.991 S18 0.905 0.9856 S19 0.9965 0.9989 S0 0.999 1.0000 S1 0.99 0.999 S 0.9916 0.998 S3 0.9793 0.9963 S4 0.977 0.9941 S5 0.9693 0.9936 S6 0.9477 1.0000 S7 0.9451 1.0000 S8 0.9337 1.0000 S9 0.955 0.9936 S30 0.90 0.9998 S31 0.9178 0.9995 S3 0.9169 0.9995 S33 0.9166 0.9853 Algorthms Te Swtches TABLE I COMPARISON OF RESULTS 33 BUS The results obtaned usng ABC method s compared wth other varous exstng methods and the results are shown n Table I. II. CONCLUSION The ABC has been used to fnd the network reconfguraton of the dstrbuton network through whch optmum power flow path (optmum swtchng sequence) has been found by satsfyng the dstrbuton network constrants. The varous constrants are radalty and voltage lmts. The proposed methodology has three stages. In stage one power flow path (te swtches) has been found usng ABC. In stage two the dentfed te swtches has been checked for radalty constrant and f the radlaty constrant s satsfed then the procedure proceed to stage three. Otherwse the process has to be repeated. In stage three load flow analyss s done. The Forward Sweeper algorthm has been used to calculate the power flow parameters. The dstrbuton constrants are checked based on the load flow analyss results. The opton whch has mnmum losses s selected as the best soluton. The proposed methodology has been appled to a 33 bus sngle feeder dstrbuton network usng MATLAB. Total Impedance Ω Total Real power loss KW Mnmum p.u. oltage Bus No of Mn p.u. oltage INITIAL S33 S34 S35 S36 S37 6.347 104.3 0.905 18 RGA[14] S7 S10 S14 S36 S37 33.7917 113.3 0.9345 33 TSA[14] S7 S9 S14 S3 S37 3.5863 1061. 0.9340 3 ABC S4 S10 S19 S5 S35 30.493 134.6 0.9853 33 * RGA - Refned Genetc Algorthm, * TSA - Tabu Search Algorthm REFERENCES [1] Merln and H. Back, Search for a mnmal loss operatng spannng tree confguraton n an urban power dstrbuton system, n Proc. 5th System Computaton Conf. (PSCC), Cambrdge, U.K., pp.1 18, 1975. [] M.E Baran and F.F Wu, Network reconfguraton n dstrbuton systems forloss reducton and load balancng, IEEE Transactons on Power Delvery, ol. 4, 01-1407. [3] R.J Safr, M.M.A Salama and A.Y Chckan, Dstrbuton system reconfguraton for loss reducton: a new algorthm based on a set of quantfed heurstc rules, Proceedngs of Electrcal and Computer Engneerng, ol. 1, Canada, 1994, pp. 15-130. [4] J-C. Wang, H-D. Chang and G. Darlng, An effcent algorthm for real tme network reconfguraton n large scale unbalanced dstrbuton systems, IEEE Transactons on Power systems, ol. 11, No. 1, 1996, pp. 5511-5517. [5] J.Z Zhu, Optmal reconfguraton of electrcal dstrbuton network usng the refned GA, Electrc Power System Research, ol. 6, 00, pp. 37-84. [6] W.M. Ln, F.S Cheng and M.T. Tsay, Dstrbuton feeder reconfguraton wth refned GA, IEEE Proc-Gener. Transmsson Dstrbuton, ol. 147, No. 6, 000, pp. 1484-1491.H. D. [7] K. Prasad, R. Ranjan, N.C Sahoo and A. Chaturved, Optmal reconfguraton of radal dstrbuton systems usng a fuzzy mutuated GA, IEEE Transactons on Power Delvery, ol. 0, No., 005, pp. 111-113. [8] H. Km, ANN based feeder reconfguraton for loss reducton n dstrbuton system, IEEE Transacton on Power Delvery, ol. 8,No. 3, 1993, pp. 1356-1366. Internatonal Scholarly and Scentfc Research & Innovaton 8() 014 401
World Academy of Scence, Engneerng and Technology Internatonal Journal of Electrcal and Computer Engneerng ol:8, No:, 014 [9] C.-H. Ln, Dstrbuton network reconfguraton for load balancng wth a colonal Petr net algorthm, IEEE Proc-Gener. Transm. Dst., ol. 150, No. 3, 003, pp. 317-34. [10] K.K L, T.S Chung, G.J Chen and G.Q. Tang, A T.S approach to dstrbuton network reconfguraton for loss reducton, Electrc Power Components and Systems, Taylor and Francs, 003. pp. 571-585. [11] D. Karaboga, "An Idea based on Honey Bee Swarm for Numercal Optmzaton," Ercyes Unversty, Engneerng Faculty, Computer Engneerng Department., Tech. Rep. TR06, pp. 1-10, 005. [1] D. Karaboga and B. Basturk, "On the Performance of Artfcal Bee Colony (ABC) Algorthm," Appled Soft Computng, vol. 8, no. 1, pp. 687-697, 008. [13] D. Karaboga and B. Akay, "A Comparatve Study of Artfcal Bee Colony Algorthm," Appled Mathematcs and Computaton, vol. 14, no. 1, pp. 108-13, 009. [14] Rayapud Srnvasa Rao, Sadhu enkata Lakshm Narasmham, Manyala Ramalnga Raju, and A. Srnvasa Rao, Optmal Network Reconfguraton of Large-Scale Dstrbuton System Usng Harmony Search Algorthm, IEEE Transactons On Power Systems, ol. 6, No. 3,pp.1080-1088 August 011. Internatonal Scence Index, Electrcal and Computer Engneerng ol:8, No:, 014 waset.org/publcaton/9997973 S. Ganesh obtaned hs B.E. n Electrcal and Electroncs Engneerng n Dr. Svanth Adthanar College of Engneerng Truchendur, Anna Unversty, Tamlnadu, Inda, n 009 and dd hs Master of Engneerng (Power Systems Engneerng) n St. Joseph s Engneerng College, Anna Unversty, Inda, n 013. He s workng as an Assstant Professor n the department of Electrcal and Electroncs Engneerng, Chandy College of Engneerng, Thoothukud, Tamlnadu, Inda. Internatonal Scholarly and Scentfc Research & Innovaton 8() 014 40