POWER SYSTEM LOADABILITY IMPROVEMENT BY OPTIMAL ALLOCATION OF FACTS DEVICES USING REAL CODED GENETIC ALGRORITHM

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1 Journal of Theoretcal and Appled Informaton Technology 3 st July 204. Vol. 65 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: POWER SYSTEM LOADABILITY IMPROVEMENT BY OPTIMAL ALLOCATION OF FACTS DEVICES USING REAL CODED GENETIC ALGRORITHM R.MEDESWARAN, 2 N.KAMARAJ,2 Department of Electrcal and Electroncs Engneerng, Thagarajar College of Engneeng, Madura, Tamlnadu, Inda E-mal: rmedeswaran@gmal.com ABSTRACT FACTS devces can effectvely control the load flow dstruton, mprove the usage of exstng system nstallatons y ncreasng transmsson capalty, compensate reactve power, mprove power qualty, and mprove staltes of the power network. However, the locaton and settngs of these devces n the system plays a sgnfcant role to acheve such enefts. Ths work presents the applcaton of Real Coded Genetc Algorthm (RGA) for fndng out the optmal locatons, and the optmal parameter settngs of sngle type and mult type FACTS devces to acheve maxmum system loadalty (MSL) n the power system. The FACTS devces used are Thyrstor Controlled Seres Capactor () and Unfed Power Flow Controller (). The reactance model for and the decoupled model for are consdered for ths work. The thermal lmts of the lne and voltage lmts of the uses are taken as constrants durng the optmzaton. Smulated Bnary Crossover (SBX) and Non-unform polynomal mutaton are employed to mprove the performance of the Genetc Algorthm used. Smulatons are performed on IEEE 6 us and 30 us power systems. The otaned results are encouragng and show the effectveness of RGA. Keywords: Loadalty, FACTS,,, and Real Coded Genetc Algorthm. INTRODUCTION Power flow over the transmsson lnes s manly lmted y some network characterstcs such as thermal lmts, stalty lmts, and voltage lmts. Such lmtatons can e removed y addng new transmsson and/or generaton capacty. However, such soluton s dffcult for envronmental, and economcal reasons. Flexle Alternatng Current Transmsson Systems (FACTS), whch s a concept proposed y N.G.Hngoran [], are desgned to remove such lmtatons. For a meshed network, optmal locaton of FACTS devces allows to control ts power flows and thus to ncrease the system loadalty. Thyrstor Controlled Seres Capactor () s a devce whch offers smooth and flexle control for loadalty enhancement wth much faster response compared to the tradtonal control devces. Most of the researchers have focused on ssues such as transent stalty mprovement, susynchronous resonance (SSR) mtgaton, dampng of power swngs, avodng voltage collapse, enhancng power system relalty, etc [2],[3]. 824 The Unfed Power Flow Controller () s a versatle controller that can e used to control actve and reactve power flows n a transmsson lne. s used to control the power flow n the transmsson systems y controllng the mpedance, voltage magntude and phase angle. Ths controller offers advantages n terms of statc and dynamc operaton of the power system[4],[5]. In ths work, an optmzaton prolem has een formulated for placng the s and s to n approprate locatons.real Coded Genetc Algorthm (RGA) has een appled to solve the prolem. The proposed approach employs the Smulated Bnary Crossover and Polynomal mutaton to mprove the performance of the Genetc Algorthm used. IEEE 6-us and 30-us are the test systems used and the results are analysed n terms of mproved loadalty.

2 Journal of Theoretcal and Appled Informaton Technology 3 st July 204. Vol. 65 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: PROBLEM FORMULATION 2. Ojectve functon Optmal locaton of FACTS devces to mprove power system loadalty has een mathematcally formulated and s gven y J () J s the factor ndcatng volaton of lne flow lmts and us voltage lmts and the ojectve s to maxmze J. NL Total numer of lnes NB Total numer of uses LF Lne flow Index ojectve functon for lne BF j Bus voltage volaton ojectve functon for us j (2) (3) LF lne BF j B L = NL NB LF = = BF j j Lne flow Index ojectve functon for Bus voltage volaton ojectve functon for us j Branch loadng (Percentage of lne flow respect to lne capacty rate) 2.2 Devce allocaton and modelng Mult type FACTS devces may have to e nstalled n order to acheve the desred performance. The soluton for the optmal allocaton prolem ncludes the optmal locaton for the devces to e nstalled and the optmal settngs of those nstalled devces so that the loadalty s maxmzed. In ths work, s modeled as a varale reactance. To avod over compensaton of the lne, the reactance value varaton s lmted etween -0.5 X L to + 0.5X L where X L s the reactance of the ranch n whch s connected. Fgure.. The Reactance Model Of For, the decoupled model s used. Ths model s composed of two separate load uses snce can control the power flow over the transmsson lne and us voltages where t s nstalled. Fgure. 2. The decoupled model of An has four varales P u, Q u, P u2, and Q u2. Wth the losses of the assumed to e neglected, the actve power flow P j that goes from us to us j can e expressed y equaton (4). An can control the power flow ut cannot generate the real power flow. So the condton of equaton (5) should e satsfed. P j = P u (4) P u +P u2 = 0 (5) Each reactve power output of the, Q u and Q u2 can e set to an artrary value wthn the capacty of to mantan the us voltages. Therefore f multple s are nstalled n the power system, the control varales of the k-th are represented as follows: k-th = [P u k Q u k P u k2 Q u k2] (6) P u k + P u k2 = 0 (7) where, P u k - st us actve power of the k-th Q u k - st us reactve power of the k-th P u k2-2 nd us actve power of the k-th Q u k2-2 nd us reactve power of the k-th 2.3 Constrants The followng are the constrants assocated wth the formulated prolem Constrant 0.5X L < X < 0.5X L (8) X L - orgnal lne reactance n per unt X - reactance offered y 825

3 Journal of Theoretcal and Appled Informaton Technology 3 st July 204. Vol. 65 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: Constrant The constrants assocated wth the decoupled model of are as follows: -00 MW P u 00 MW (9) P u2 = - P u (0) 00 MVAR Q u 00 MVAR () -00 MVAR Q u2 00 MVAR (2) where P u, P u2 are the real power njected nto the system. Q u, Q u2 are the reactve power njected nto the system Voltage Stalty Constrants The us voltage V must le wthn the followng lmts and V S represents the voltage volaton. 0 Vs = 0.9 V V Power alance Constrants (3) P G = P D +P L (4) P = Σ E E k [ G k cos(θ -θ k )+B k sn(θ -θ k )] (5) Q = Σ E E k [ G k sn(θ -θ k )-B k cos(θ -θ k )] (6) Where θ,θ k - Phase angles at uses and k respectvely. E,,E k - Voltage magntudes at us and k respectvely. G k,,b k - Elements of Y Bus matrx. The us voltage V must le wthn the followng lmts and V S represents the voltage volaton. 3. ALGORITHM f 0.9 < V <. f V < 0.9 f V 3. Real coded Genetc Algorthm Genetc algorthm s a knd of stochastc search technque ased on the mechansm of natural Selecton and survval of the fttest [6],[7]. GA has ts superor roust ehavour n nonlnear envronments over the other optmzaton technques. The archtecture of >. the GA mplementaton can e segregated nto the followng three consttuent phases namely: ntal populaton generaton, ftness evaluaton and genetc operatons[0]. It has een wdely confrmed that realnumer encodng performs etter than nary or gray encodng for constraned optmzaton. Owng to the adaptve capalty, SBX crossover and polynomal mutaton operators are employed. Tournament selecton s used as selecton mechansm n order to avod premature convergence 3.. Smulated Bnary Crossover In SBX crossover [3], two offsprng solutons are created from two parents as follows: ( 2u ) ηc+, u 0.5 βq = ηc+ ( ), otherwse 2 u (7) Then compute the offsprng x x x (, t+ ) (,t+ ) ( 2,t+ ) and x (, t ) ( 2, t) [( + β ) x + ( β ) x ] [ β x + + β x ] ( 2, t+ ) (, t ) ( 2, t ) ( ) ( ) (8) = 0.5 = 0.5 q q 3..2 Non-unform Polynomal Mutaton Newly generated offsprng undergoes polynomal mutaton operaton. The new offsprng s determned as follows: y (, t+ ) (9) (, t+ ) U L = x + ( x x ) δ The parameter δ s calculated from the polynomal proalty dstruton. ηm p( δ ) = 0.5( η + )( δ ) m (20) ηm+ ( 2r ), δ = - [ 2( - r )] η m (2) η m s the mutaton ndex., f r 0.5 q f r < 0.5 q 826

4 Journal of Theoretcal and Appled Informaton Technology 3 st July 204. Vol. 65 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: RESULTS AND DISCUSSION The algorthm s mplemented usng MATLAB 7.0 on a PC wth a Pentum IV processor and GB RAM. To otan the optmzed output from the algorthm, ten trals wth ndependent populaton ntalzatons have een done.to acheve more effectveness, the maxmum numer of functon evaluatons s set at 0, Parameter Tunng for RGA Populaton sze =00 Crossover proalty Pc=0.8 Mutaton proalty, Pm = /numer of varales Crossover ndex, η c =5 Mutaton ndex, η m = Test Systems Load ncreasng studes on the real power system are done for dfferent ams n plannng and operaton process of the system. In ths work, the algorthm s mplemented n IEEE 6-us and IEEE 30-us test systems. The followng three cases are consdered for the analyss and the results are presented efore and after placng the FACTS devces. CASE : actve power at any one load us. CASE 2: actve power at any two load uses. CASE 3: actve power at all load uses 4.3 Ieee 6 Bus System 4.3. efore placng the devce The ase loads are ncreased gradually and the power flow s performed for every ncrement of the load.the ncrement of the load contnues untl the lne flow or us voltage volaton occurs. Tale I shows the maxmum system loadalty that can e acheved for the ase case system wthout any FACTS devce. Tale : The Maxmum System Loadalty Before Placng Any Devce actve Maxmum system power at us loadalty n % & & &6.86 4,5& after placng one upfc and one tcsc CASE : actve power at any one load us. In ths case, the real power demand s ncreased gradually n any one of the load uses. The proposed algorthms yeld the sutale locatons to nstall the and so that the maxmum loadalty s acheved. The settngs of the devces and the percentage of loadalty have een presented n Tale 2. The load at us 6 can e ncreased wthn thermal lmts of the lne and voltage lmts of the uses, wth the maxmum loadalty of 40.94% y nstallng one and one n th and 7 th locaton respectvely, wth the recommended settngs gven y RGA. CASE 2: actve power at any two load uses. In ths case, the real power demand s ncreased gradually n any two load uses. The optmum locatons and settngs for the FACTS devces to e nstalled, and the percentage of maxmum loadalty that can e acheved have een presented n Tale 3. The load at us 4 and 5 can e ncreased wthn thermal lmts of the lne and voltage lmts of the uses, wth the maxmum loadalty of 35.43% y nstallng one and one n th and 2 nd locatons respectvely, wth the recommended settngs gven y RGA. CASE 3: actve power at all load uses. Here, actve load s ncreased at us 4, us 5 and us 6 smultaneously. Tale 4 shows the locatons and settngs of and to acheve the maxmum loadalty. It s oserved that y placng the devces n proposed locatons wth the recommended 827

5 Journal of Theoretcal and Appled Informaton Technology 3 st July 204. Vol. 65 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: settngs y RGA, the maxmum loadalty of 24.43% can e acheved. Fgure.3 llustrates the comparson of maxmum system loadalty of IEEE 6 us system for dfferent cases. actve power at us Tale 2 : The Maxmum System Loadalty After Placng One And One For Case Real coded Genetc Algorthm Locaton actve power at us 4&5 settngs P u Q u P u Q u P u Q u P u Q u P u Q u P u Q u Locaton settngs (pu) Loadalty n % Tale 3: The Maxmum System Loadalty After Placng One And One For Case 2 Real coded Genetc Algorthm Locaton 5&6 4&6 settngs P u Q u.905 P u Q u P u Q u P u Q u P u Q u P u Q u Locaton 3 settng (pu) Loadalty n % actve power at us Tale 4: The Maxmum System Loadalty After Placng One And One For Case 3 Real coded Genetc Algorthm Locaton 4, 5 &6 settngs P u Q u P u Q u Locaton settng (pu) Loadalty n %

6 Journal of Theoretcal and Appled Informaton Technology 3 st July 204. Vol. 65 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: IEEE 30 Bus System The IEEE 30-us system conssts of slack us, 5 generator uses, 24 load uses and t conssts of 43 ranches. To smulate ths case, actve load s ncreased at us 4, us 5 and us 6 smultaneously. Tale 5 shows the locatons and settngs of and to acheve the maxmum loadalty for case 3. By placng the devces n proposed locatons wth the recommended settngs y RGA, the maxmum loadalty of 36.72% can e acheved. actve power at us Tale 5: The Maxmum System Loadalty After Placng One And One For Case 3 Real coded Genetc Algorthm Locaton 4, 5 &6 2 settngs P u Q u.52 P u Q u Locaton settngs (pu) Loadalty n % effectve control of the load flow dstruton to mprove the usage of exstng system nstallatons y ncreasng transmsson capalty. The comparson of results shows the effectveness of the proposed algorthm n terms of enhanced loadalty. Fgure.3 Comparson Of Maxmum System Loadalty (MSL) For IEEE 6 Bus System 5. CONCLUSION Ths work presents the applcaton of Real coded Genetc Algorthm for fndng out the optmal locatons and the optmal settngs of Mult-type FACTS devces, and, to acheve maxmum system loadalty n the power system. The thermal lmts of the lnes and the voltage lmts of the uses are taken as constrants durng the optmzaton. Smulated Bnary Crossover and Non-unform Polynomal mutaton have een adopted for enhancng the performance of RGA. Smulatons have een performed on IEEE 6-us and IEEE 30-us power systems for three dfferent cases. The dfference cases are consdered n ths work to perform loadalty. The results show the REFRENCES: [] N. G. Hngoran, Power electroncs n electrcal utltes: role of power electroncs nfuture power systems, Proceedngs of the IEEE, Vol. 76 No. 4, Aprl 988, pp [2] M. Saravanan et al, Applcaton Of PSO Technque For Optmal Locaton Of FACTS Devces Consderng System Loadalty And Cost Of Installaton, Electrc Power system research, July [3] Y. Xao, Y. H. Song, C. C. Lu, and Y.Z.Sun, Avalale transfer capalty enhancement usng FACTS devces, IEEE Transactons on Power Systems, vol. 8, no., Feruary [4] Adel salam and aly, Optmal locaton of n electrcal Power systems, IEEE conference proceedng, [5] S. Gerex, R.Cherkaou, and A.J.Germond, Optmal locaton of mult-type FACTS devces y means of genetc algorthms, IEEE Transacton on Power-Systems, vol. 6, pp , 200. [6] Golderg, D. E., Genetc Algorthms n Search Optmzaton and Machne Learnng, Addson-Wesley Pulshng Company, Inc,

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