Voltage Stability in Power System A Survey S.Jaisiva 1 S.Neelan 2 K.Arul Selvi 3 R.Vinoth 4

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1 IJSRD - Internatonal Journal for Scentfc Research & Development Vol. 3, Issue 04, 2015 ISSN (onlne): S.Jasva 1 S.Neelan 2 K.Arul Selv 3 R.Vnoth 4 1,2,4 Assstant Professor 3 Assocate Professor 1,2,3,4 Department of Electrcal & Electroncs Engneerng 1,2,3,4 IFET College of Engneerng, Vllupuram, Tamlnadu, Inda Abstract Reactve power optmzaton (RPO) s an mportant ssue for provdng the secure and economc run of the power systems. The mportance of reactve power plannng on economc proft and secure runnng has been ncreasng, because of the ncreasng fuel costs and nvestment funds. It s also qute mportant for an electrc operator to provde voltage n a specfed range for the customers. As such, RPO provdes voltage control n power systems. Furthermore, t s used for decreasng actve power loss and makng better power flow by enhancng the voltage stablty. The voltage stablty ndex s used to fnd optmal locaton of the facts devces n the network. Voltage stablty problems usually occur n heavly loaded systems. Nowadays the power demand ncreases enormously, hence n a large nterconnected power system network subject stress condton. Ths stuaton can be handled by ncreasng the generaton or reduce transmsson losses. Through varous uses of FACTS devces n transmsson lnes, the voltage stablty profles s mantaned and by use of algorthms lke PSO, GA, SFLA the voltage stablty and the profle of the system has been mproved n transmsson lnes. Key words: Voltage Profle, Voltage Stablty, UPFC, TCSC, SVC, STATCOM, Shuffled Frog Leap Algorthm (SFLA), Bacteral Foragng Algorthm (BFA), Genetc Algorthm (GA), Partcle Swarm Optmzaton (PSO) I. INTRODUCTION Reactve power optmzaton s one of the dffcult optmzaton problems n power system operaton and control. To mprove the voltage profle and to decrease the actve power losses along the transmsson lnes under varous operatng condtons, power system operator can select a number of control tools such as swtchng reactve power sources, chargng generator voltages and adjustng transformer tap settngs. The mult- objectve of ths paper s to allocate reactve power sources so that the actve power transmsson loss s to be mnmzed and the voltage stablty margn s to be maxmzed, whle satsfyng the number of constrants. Reactve power optmzaton (RPO) s an mportant optmzaton process n terms of voltage stablty, voltage qualty and actve power loss. The man object functon n RPO s the total actve power loss functon, but n later years, systems were analyzed n terms of reactve supply costs and voltage profle. Snce voltage profle mnmzes the devatons between the nomnal and bus voltages, the reactve power transferred from the bus wll decrease and the lnes current wll also decrease. As such, these provde supplements for the decrement of actve power loss. The effects of the reactve power supples connected to buses are qute mportant on reducton of actve power loss. However, due to the nstallaton, some extra equpment and devces costs lke dsjoncteur needs to be addressed from a dfferent perspectve. Based on ths, cost functons are used n RPO, leadng to a mult- objectve functon. Ths functon s a non lnear functon havng a lot of varables and constrants. Frstly, ths problem was solved wth classcal methods such as lnear, non-lnear, quadratc and dynamc programmng. Snce the problems have a lot of varables, constrants, dfferent local mnmum and the possblty of gettng trapped n the local mnmum, the metaheurstc methods are preferred as the soluton to these problems. Consumpton of electrcty s ncreasng wth economc development wth deregulated electrcty market. Power system stablty s defned as the ablty of a power system that enables t to reman n stable operatng equlbrum under normal operatng condtons and to regan an acceptable state of equlbrum after beng subjected to a dsturbance. Voltage stablty depends on generator reactve power lmt, load characterstcs and transmsson lnes. The power system voltage s stable f voltages after dsturbances are close to voltages at normal operatng condtons. As the load vares, the reactve power requrements of the transmsson system also vary. Snce the reactve power cannot be transferred over long dstances and losses also ncreases. The proper selecton and locaton of the FACTS devces for controllng the reactve power and voltage are major challenges of power system. To overcome voltage nstablty and power losses, FACTS devces have been mplemented n power system. Thus by mplementng FACTS devces, the transmsson losses were reduced whch enhances the voltage profle. Optmzaton algorthms can be ether determnstc or stochastc. The OPF methods are conventonal and ntellgent and solved by varetes of methods such as successve lnear programmng, Newton based nonlnear programmng method and wth varetes such as recently proposed nteror pont methods. The use of optmzaton algorthms n transmsson lnes makes to know the maxmum voltage profle place and through obtanng t, the FACTS devces are used to enhance the voltage profle. By ncorporatng FACTS devces wth best optmal algorthms, the voltage stablty enhancement s acheved. Voltage Stablty s becomng an ncreasng source of concern n stablty operaton of present day power systems. The problem of voltage nstablty s manly consdered as the nablty of the network to meet the load demand mposed n terms of nadequate reactve power support or actve power transmsson capablty or both. Voltage collapse s a local load bus problem and depends mostly on load condtons n the system. Thus the reactve power support and voltage problems are ntrnscally related. Hence ths paper formulates the reactve power optmzaton as a mult-objectve optmzaton problem wth loss mnmzaton and voltage stablty margn maxmzaton objectves. The statc voltage stablty margn s prmarly assocated wth the reactve power support. Several tools All rghts reserved by 330

2 have been presented n the lterature for the analyss of the statc voltage stablty of a system. II. PROBLEM FORMULATION A mult-objectve optmzaton problem conssts of multple objectve functons wth equalty and nequalty constrants to be optmzed. The equalty constrants represents the typcal load flow equatons. A. Voltage stablty ndex: Voltage stablty s an mportant problem to power system. Voltage Stablty s evaluated at each bus of the system by an ndcator, L ndex. At load bus j, L ndex can be expressed as L j = L j = 1 - j L Where L set of load buses G set of generator buses V j complex voltage at load bus j V complex voltage at generator bus C j elements of matrx C whch can be determned as = - -1 Sub matrces of Y bus matrx are [YLL] and [YLG] and t can be found usng The frst objectve functon consderng the mnmzaton of Voltage stablty Index can be represented as gven F 1 = Voltage Stablty Index = L max Ths analyss wll be carred out only for the load buses; hence the ndex that to be obtaned s for load buses only. For stablty the ndex L must not be more than 1 for any of the nodes j. The global ndex for stablty of the gven power system s defned to be L= maxmum of Lj for all j (load buses). The ndex far away from 1 and close to 0 ndcates voltage stablty. The L ndex wll gve the scalar number to each load bus. Among the varous ndces for voltage stablty and voltage collapse predcton (.e. far away from 1 and close to 1 or >1 respectvely), the L ndex wll gve more accurate results. The L ndces for gven loads condtons are calculated for all load buses and the maxmum of the L ndces gves the proxmty of the system to voltage collapse. B. Power loss (MW): The second objectve functon consderng the mnmzaton of real power loss can be expressed as, F3 = mn F(Pn) = F 2 G b P Gj - C Where n no of Generators P G Generated power of th generator a cost coeffcent of th generator b cost coeffcent of th generator c cost coeffcent of th generator D. Ftness functon: Consderng all the three objectve functons from (1) - (6) the Ftness Functon(FF) s expressed as, F = h 1 F 1 + h 2 F 2 + h 3 F 3 Where h1, h2 and h3 are weghtng factor of mnmzaton of VSI objectve functon, weghtng factor of power loss mnmzaton objectve functon, weghtng factor of generaton cost mnmzaton objectve functon. h 1 F 1 + h 2 F 2 + h 3 F 3 =1 where h 1, h 2 and h 3 are coeffcents. By tral and error method, they are optmzed to 0.3, 0.3 and 0.4 for SVC and 0.35, 0.35 and 0.3 for TCSC by satsfyng the above equaton. III. REVIEW USING FACTS DEVICE A. Unfed Power Flow Controller (UPFC) The UPFC s a unque devce that can provde smultaneous control of all basc power system parameters. The UPFC can ndependently and very rapdly control both real and reactve power flows n a transmsson lne. It can fulfll the functons of reactve shunt compensaton, seres compensaton and phase shftng meetng multple control objectves. UPFC has seres and shunt connected converters. The UPSC can control the lne s real power and reactve power and bus voltage where t s connected, by proper njecton of voltage magntude n seres and shunt respectvely. Here the reactve power wll be njected at the lnes whenever requred. The UPFC operates wth constrants on the followng varables such as the seres njected voltage magntude; the lne current through seres converter; the shunt- converter current the mnmum lne sde voltage of the UPFC and the real power transfer between the seres converter and the shunt converter. The effect of controlled voltage Vs on system s, V = V s + V P loss =,j (V 2 + V j 2 2V V j cos( 1-2 ) Where V voltage magntude at bus g,j conductance of lne -j voltage angle at bus N L Total no of transmsson lnes C. Fuel cost: The thrd objectve functon consderng mnmzaton of cost of generaton can be expressed as, Fg. 1: Basc arrangement of UPFC B. Thyrstor Controlled Seres Reactor (TCSC) TCSC s seres connected FACT devce n whch a capactor s connected n transmsson lne and a parallel connecton of thyrstor controlled nductor wth capactor. The power flows n heavly loaded lne can be reduced by TCSC through power flow control n the network. When the L>(1/C), the reactance of the FC s less than that of the parallel connected varable reactor and that ths combnaton All rghts reserved by 331

3 provdes a varable capactve reactance are both mpled. Moreover, ths nductor ncreases the equvalent capactance reactance of the LC combnaton above that of the FC. In the varable capactve mode of the TCSC, as the nductve reactance of the varable nductor s ncreased, the equvalent capactve reactance s gradually decreased. The behavour of the TCSC s smlar to that of the parallel LC combnaton. The dfference s that the LC combnaton analyss s based on the presence of pure snusodal voltage and current n the crcut, whereas n the TCSC, because of the voltage and current n the FC and thyrstor controlled reactor are not snusodal because of the thryrstor swtchng. Fg. 2: Basc model of TCSC C. Statc Var Compensator (SVC) SVC s a VAR generator whose output s adjusted to exchange capactve or nductve currents so as to mantan/ control bus voltage. SVC s combnaton of controllable shunt reactor and a shunt capactor. The susceptance of SVC can be vared by varyng frng angle of thyrstor n range of The SVC slope substantally reduces the reactve power ratng of the SVC for achevng nearly the same control objectves; prevents the SVC from reachng ts reactve power lmts too frequently; and facltates the sharng of the reactve power among multple compensators operatng n parallel. When more than one compensator s used at one locaton, the control acton must be coordnated. Wth addtonal balancng controls, exact load sharng can be attaned. The SVC behaves lke a controlled susceptance, and ts effectveness n regulatng the system voltage s dependent on the relatve strength of the connected ac system. Fg. 4: Basc structure of STATCOM IV. REVIEW USING OPTIMIZATION ALGORITHM A. Shuffled frog leap algorthm (SFLA) The SFLA - based approach for solvng the optmal placement and szng of dstrbuted generaton problem to mnmze the loss and mprove the voltage profle takes the followng steps: In SFLA, each possble soluton X = (x1, x2, x3,, xs) that n ths paper X = l1, l2,. lbus, x1, x2,.. x power lmt.where,1 s the number of DG locaton canddates and x s the number of capacty types of DGs are s consdered as a frog. The steps of the algorthm are as follows: Step - 1: Create an ntal populaton of P frogs generated randomly. SFLA populaton = [X1, X2,., Xp] pxn where, p= mxn, N s the number of DG, m s the number of memplexes and n s the number of frogs n memplex. Step - 2: Sort the populaton ncreasngly and dvde the frogs nto m memplexes each holdng n frogs such that P = mxn. The dvson s done wth the frst frog gong to the frst memplex, second one gong to the second memplex, the m th frog to the m th memplex and the m+1th frog back to the frst memplex. Below fgure llustrates ths memplex parttonng process. Fg. 3: Basc model of SVC D. Statc Compensator (STATCOM) STATCOM s the voltage source nverter whch converts a DC nput voltage nto AC output voltage n order to compensate the actve and reactve power needed by system. STATCOM exhbts constant current characterstcs when voltage s low/hgh under/ over the lmt. Ths allows STATCOM to delver constant reactve power to system. A STATCOM s a controlled reactve power source. It provdes the desred reactve power generaton and absorpton entrely by means of electronc processng of the voltage Fg. 5: Memplex parttonng process Step - 3: Wthn each constructed memplex, the frogs are nfected by other frogs deas; hence they experence a me-metrc evoluton. Memetrc evoluton mproves the qualty of the meme of an ndvdual and enhances the ndvdual frog s performance towards a goal. Below are detals of memetc evoluton for each complex: Step 3-1: Set m1=0 where m1 counts the number of memplexes and wll be compared to the total number of memplexes wth m. Set y1=0 where y1 counts the number of evolutonary steps and wll be compared wth the maxmum number of steps (ymax), to be completed wthn each memplex. Step 3-2: Set m1=m1+1 Step 3-3: Set y1=y1+1 Step 3-4: For each memplex, the frogs wth the best ftness and worst ftness are dentfed as X w All rghts reserved by 332

4 and X b respectvely. Also the frog wth the global best ftness Xg s dentfed, and then the poston of the worst frog Xw for the memplex s adjusted. Below fgure demonstrates the orgnal frog leapng rule. If the evolutons produce a better frog(soluton), t replaces the older frog, otherwse X b s replaced by Xg and the process s repeated. If no mprovement becomes possble n ths case a random frog s generated whch replaces the old frog. Fg. 6: The orgnal frog leapng rule Step 3-5: If m1<m, return to Step 3-2. If y1<ymax, return to step 3-3, otherwse go to Step2. Step 4: Check the convergence. If the convergence crtera are satsfed stop, otherwse consder the new populaton as the ntal populaton and return to the step 2. The best soluton found n the search process s consdered as the output results of the algorthm. The flowchart of the SFLA s llustrated n below fgure. B. Partcle Swarm Optmzaton (PSO): PSO s a well known novel optmzaton method developed by Kennedy and Eberhart and s beng used n dfferent felds for optmzaton. W can be calculated as, W = Wmax x ter The range of W s 0.4 to 0.9. The steps to obtan optmal locaton s, Step - 1: Intal searchng ponts and veloctes are randomly generated wthn ther lmts. Step - 2: P best s set to each ntal searchng ponts. The best evaluated values among P bests are set to g best. Step - 3: New veloctes are calculated usng equaton V d (t+1) = W+ V d (t) + C 1 * rand() (P bestd X d (t)) + C 2 * rand () * (g bestd X d (t)) Step - 4: If V d (t+1) < V dmn Then V d (t+1) = V dmn and f V d (t+1) > V dmax, then V d (t+1) = V dmax Step - 5: New searchng ponts are calculated usng X d (t+1) = X d (t) + V d (t+1) Step - 6: Check capacty lmts constrants, If P d (t+1) > P dmax, then P d( t+1) = P dmax and f P d (t+1) < P dmn then P d (t+1) = P dmn Step - 7: Evaluate ftness values for new searchng pont. If evaluated value of each agent s better than prevous P best then set to P best. If best P best s better than g best then set to g best. Step - 8: If maxmum teraton s reached stop process otherwse go to step 3. C. Bacteral Foragng Algorthm (BFA): Foragng theory s based on the natural behavor of anmal searchng for ther nutrent whch maxmze ther energy for foragng [10]. Ths algorthm s based on the searchng behavor of E.Col bactera. E.col s a mcroorgansm whch has the nature of searchng of food more qucker than other. Chemotaxs s the natural foragng behavor of bactera, whch helps to catch the requred nutrent. Implementaton of chemotaxs steps, the searchng process are followed. Let j be the steppng rate of chemotaxs, k be the reproducton step and l be the ndex of elmnaton dspersal event. The length of lfe tme of bactera Nc s measured by the number of chemotoxx steps. Bactera swms n the free space to reduce loss, along wth maxmum number of steps Ns. Next to the chemotaxs reproducton s adopted. Nre s the number of reproducton steps to be taken by bactera for populaton sortng. Inorder to make ncrease of populaton of bactera reproducton s carred out. Ths method provdes bactera wth a lot of nutrents and also keeps the populaton sze constant. For ntalzaton, you must choose p, S, Nc, Ns, Nre, Ned, ped, and the C( ), = 1,2,K, S. If you use swarmng, you wll also have to pck the parameters of the cell-to-cell attractant functons; here we wll use the parameters gven above. Also, ntal values for the θ, = 1,2,K, S, must be chosen. Choosng these to be n areas where an optmum value s lkely to exst s a good choce. Alternatvely, you may want to smply randomly dstrbute them across the doman of the optmzaton problem. The algorthm that models bacteral populaton chemotaxs, swarmng, reproducton, elmnaton, and dspersal s gven here (ntally, j = k = l = 0). For the algorthm, note that updates to the θ automatcally result n updates to P. Clearly, we could have added a more sophstcated termnaton test than smply specfyng a maxmum number of teratons. Algorthm were as follows, STEP 1: Elmnaton-dspersal loop: l= l + 1 STEP 2: Reproducton loop: k = k + 1 STEP 3: Chemotaxs loop: j = j + 1 For = 1,2,K,S, take a chemotactc step for bacterum as follows. Compute J(,j,k,l). Let J(,j,k,l) = J(, j,k,l)+ J cc(θ(j,k,l), p( j,k,l )) (.e., add on the cell-to-cell attractant effect to the nutrent concentraton). Let J last = j(,j,k,l) to save ths value snce we may fnd a better cost va a run. Tumble: Generate a random vector Δ( ) _p wth each element m(),m = 1,2,K,p, a random number on [ 1,1]. Move: Let ( j 1, k, l) ( j, k, l) C( ) () T ( ) ( ) Ths results n a step of sze C() n the drecton of the tumble for bacterum. Compute J(, j + 1,k,l), and then let J(, j + 1,k,l) = J(,j+1,k,l)+Jcc(θ(j+ 1,k,l),P(j+1,k,l)). Swm (note that we use an approxmaton snce we decde swmmng behavor of each cell as f the bactera numbered {1,2,K,} have moved and { + 1, + 2,K, S} have not; ths s much smpler to smulate than smultaneous decsons about swmmng and tumblng by all bactera at the same tme. Let m=0 (counter for swm length). Whle m<ns (f have not clmbed down too long) Let m=m+ 1. All rghts reserved by 333

5 If J(,j+1,k,l) <Jlast (f dong better), let Jlast= J(,j+1,k,l) and let ( j 1, k, l) ( j 1, k, l) C( ) () T ( ) ( ) Else, let m= Ns. Ths s the end of the whle statement. Go to next bacterum ( + 1) f S (.e., go to b) to process the next Bacterum). If j < Nc, go to step 3. In ths case, contnue chemotaxs, snce the lfe of the bactera s not over. 1) Reproducton: a) For the gven k and l, and for each = 1,2,K, S, let be the health of bacterum (a measure of how many nutrents t got over ts lfetme and how successful t was at avodng noxous substances). Sort bactera and chemotaxs parameters () n order of ascendng cost Jhealth (hgher cost means lower health). Nc1 J health J (, j, k, l) j1 b) The Sr bactera wth the hghest Jhealth values de and the other Sr bactera wth the best values splt (and the copes that are made are placed at the same locaton as ther parent). STEP 4: If k < Nre, go to step 2. In ths case, we have not reached the number of specfed reproducton steps, so we start the next generaton n the chemotaxs loop. STEP 5: Elmnaton-dspersal: For = 1,2,K, S, wth probablty ped, elmnate and dsperse each bacterum (ths keeps the number of bactera n the populaton constant). To do ths, f you elmnate a bacterum, smply dsperse one to a random locaton on the optmzaton doman. STEP 6: If l<ned, then go to step 1; otherwse end. D. Evolutonary Programmng (EP) EP s an artfcal ntellgence method whch s an optmzaton algorthm based on the mechancs of natural selectons-mutaton, competton and evoluton. The general process of EP s descrbed n [L.L.La and J.T.Ma, 1997,Kalyanmoy Deb, 2001]. The procedure of EP for RPP s brefed as follows Intalzaton: The ntal control varable populaton s selected randomly from, =1,2,...,m, where m s the populaton sze, from the sets of unform dstrbuton rangng over [ ],[ ]and [ ]. The ftness score s obtaned by runnng Newton Raphson power flow. Statstcs: The values of maxmum ftness, mnmum ftness, sum of ftness and average ftness of ths generaton are calculated. Mutaton: Each p s muted and assgned to P +m n accordance wth the followng equaton P +m,j = P,j + N(0, (X jmax - X jmn ) ), j = 1,2,..n Where, Pj denotes j th element of the th ndvdual. N( ) represents a Gaussan random varable wth mean and varance ; f max s the maxmum ftness of the old generaton whch s obtaned n statstcs. X j max and X j mn are the maxmum and mnmum lmts of the j th element. s the s the mutaton scale whch s gven as 0 1. If any P +m,j, j = 1,2,..n, varables, exceeds ts lmt, where n s the number of control P +m,j, wll be gven the lmt value. The correspondng ftness f +m s obtaned by runnng power flow wth P +m A combned populaton s formed wth the old generaton and the mutated old generaton. Competton: Each ndvdual, P n the combned populaton has to compete wth some other ndvduals to get ts chance to be transcrbed to the next generaton. Determnaton: The convergence of maxmum ftness to mnmum ftness s checked. If the convergence condton s not met, the mutaton and competton processes wll run agan. V. RESULTS In ths paper an analyss of Voltage stablty n the power system has been taken nto account. Here the analyss s based upon ncorporatng varous facts devces and optmzaton algorthms. The voltage magntudes of varous are gven as follows and all the voltage magntudes values are gven n per unt (p.u). A. By usng Facts Devces UPFC p.u TCSC p.u SVC p.u STATCOM p.u B. By usng Optmzaton Algorthms SFLA p.u BFA p.u EP p.u PSO p.u VI. CONCLUSION In ths paper ncorporaton of varous optmzaton technque and facts devces to enhance the voltage stablty n the power system has been revewed. Through the optmzaton technques the voltage stablty n the transmsson lnes are mproved such that the power flow can be enhanced n the system along wth better mprovement of voltage profle. The facts devces are used n the system on the transmsson sector to nject the reactve power whenever the reactve defcency s occurred due to ths voltage profle has mproved n a vast manner and voltage stablty has be acheved. Stll whenever the load s ncreased, the voltage profle wll get affected severely and voltage stablty starts decreasng and t can be overcome by ncorporatng Facts devces along wth optmzaton technques whch s very much useful n mprovng voltage stablty as well as the voltage profle of the power system network. All rghts reserved by 334

6 ACKNOWLEDGEMENT I would lke to express my greatest grattude to the people who have helped & supported me throughout my journal paper. Specal thanks for our charman Mr.K.V.Raja, vce charman Mr. A. Mohammed lyas, Secretary Mr.K.Shvram Alva. I extend my thanks to our Prncpal Dr.G.Mahendran, Vce Prncpal Prof S.Matlda, Dean Placement Prof J.Asha, Head - Funded projects Prof P.Pugazhendran, Head - Electrcal & Electroncs Engneerng Prof P.Nammalvar. I wsh to thank my parents for ther undvded support and nterest who nspred me and encouraged me to go my own way, wthout whom I would be unable to complete my journal. At last but not the least I want to thank my frends who apprecated me for my work and motvated me and fnally to God who made all the thngs possble. REFERENCES [1] M. Kowsalya, K.K. Ray and D.P.Kothar, Loss optmzaton for voltage stablty Enhancement Incorporatng UPFC usng PSO, Journal of Electrcal Engneerng and Technology, Vol.4 No.4,pp ,2009. [2] S. Sakthvel, Dr. D. Mary, Reactve power optmzaton for voltage stablty Lmt mprovement ncorporatng TCSC devce through DE/PSO under contngency condton, IU-JEEE, Vol.12(1),2012. [3] P.B. Chennaah, N. Krshna Kshore, Dr.M. Suryakalavath, Real power Loss mnmzaton and Voltage stablty Lmt Enhancement by usng Shuffled Frog Leap Algorthm,IOSR Journal of Electrcal and Electroncs Engneerng, Vol.9, Issue 2, Ver. III, Mar- Apr [4] A.S. Sddqu, Tanmouy Deb, Voltage Stablty Enhancement through Statc Var Compensator, Internatonal Journal of Scentfc and Engneerng research Volume 4 Issue 2, February [5] A. Anbarasan, M. Y. Sanavullah, Voltage Stablty Improvement n power system usng STATCOM, Internatonal Journal of Engneerng Scence and Technology, Vol.4, No.11 Nov [6] S. K. Dheebka, Dr. R. Kalavan, Enhancement of voltage Stablty by SVC and TCSC usng Genetc Algorthm, 2014 IEEE ICIET 14. [7] R. Muthu Kumar, K. Thanushkod, FVSI Based Identfcaton and Plannng of Reactve Power n real power system usng EP, European Journal of Scentfc Research, Vol,77, No.2,2012. [8] C. W. Taylor, Power System Voltage Stablty. New York: McGraw-Hll, 1994P. [9] Elango. K.et al, Power Transmsson Congest on Management n Restructured Power System by FACTS Devces, Generaton Reschedulng and Load Sheddng usng Evolutonary Programmng European Journal of Scentfc Research, Vol.56 No.3, pp ,2011. [10] S. Charles Raja et al, Transmsson Congeston Management n Restructured Power Systems Proceedngs of ICETECT, [11] Kessel and H. Glavtsch, "Estmatng the Voltage Stablty of Power System," IEEE Transacton on Power Delvery, Vol. PWRD-1, No. 3, pp , [12] H. Wan, J. D. Mc Calley, V. Vttal, Rsk Based Voltage Securty Assessment, IEEE Transacton on Power Systems, Vol. 15, No. 4, pp , [13] Feng Dong,et al, Improvng Voltage Stablty by Reactve Power Reserve Management IEEE Transactons on Power Systems, Vol. 20,No.1, pp , [14] Florn Captanescu, Assessng Reactve Power Reserves wth Respect to Operatng Constrants and Voltage Stablty, IEEE Transactons on Power Systems, Vol. 26, No. 4, pp , [15] P.Nagenra,et al, OPF Based Global Voltage Stablty Assessment of a Practcal Power System Incorporatng TCSC Controller, J.Electrcal Systems, Vol.7,No.1,pp D.B. Fogel. Evolutonary Computaton: Toward a new Phlosophy n Machne Intellgence, IEEE Press, [16] N G. Hngoran, L. Gyugy, Understandng FACTS: Concepts and Technology of Flexble AC Transmsson Systems, IEEE Press, New- York, All rghts reserved by 335