Improvement of Power System Condition by Placement of Flexible Alternating Current Transmission Systems devices

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1 Improvement of ower System Condton by lacement of Flexble Alternatng Current Transmsson Systems devces Mehd afar Department of Electrcal Engneerng, Marvdasht Branch, Islamc Azad Unversty, Marvdasht, Iran Motaba Abbas Department of Electrcal Engneerng, Marvdasht Branch, Islamc Azad Unversty, Marvdasht, Iran Mostafa Abbas Department of Electrcal Engneerng, Marvdasht Branch, Islamc Azad Unversty, Marvdasht, Iran ABSTRACT Flexble Alternatng Current Transmsson Systems (FACTS) devces have been used for several targets n power system, one of the man contrbutons of the devces s mprovng operaton condtons. In ths paper, two types of these devces have been placed to rase voltage profle, mnmzng system loss and arsng loadablty. The suggested FACTS devces are: Unfed ower Flow Controller (UFC) and Statc Var Compensator (SVC). Ths problem has been solved by a novel ntellgent algorthms. Smulaton has been performed on MATLAB software envronment. Capablty of the proposed model has been confrmed by testng on 30-bus IEEE test system. Keywords FACTS, UFC, SVC, power system. 1. ITRODUCTIO Flexble AC Transmsson Systems (FACTS) devces provde a means to mprove on the utlzaton of exstng nfrastructure, wth the capablty of provdng reactve power support, redrectng power flows, and mprovng utlzaton of avalable lne ratngs. Some common FACTS devces nclude the Statc VAR Compensator (SVC), statc synchronous compensator (STATCOM), Thyrstor- Controlled Seres Capactor (TCSC), and the unfed power flow controller (UFC) [1-2]. Ref.[3] presents a generalzed approach for determnaton of optmal locatons for placement of FACTs devces n the power system wth an obectve of reducng real power loss and to reduce overloadng of the lnes. In [4], the methodology for proper placement of FACTS devces n the restructured electrcty market s proposed. An effcent and relable evolutonary programmng (E) and dfferental evoluton (DE) technques based economc dspatch for pool electrcty market s used to mprove the socal welfare. In [5], applcatons of SO for solvng FACTS allocaton problem are deeply revewed from perspectve of the obectves, used basc SO varant, parameter selecton, mult-obectve handlng strategy, constrant handlng strategy and dscrete varable handlng strategy. The plannng problem n [6] s formulated as a sngle obectve optmzaton problem where the real power loss and bus voltage devatons are mnmzed under dfferent loadng condtons. Gravtatonal Search Algorthm (GSA) based optmzaton algorthm and partcle swarm optmzaton technques (SO) are appled on IEEE 30 bus system. In [7], BSOA s employed to fnd optmal locaton and settng of FACTS devces. SVC s and thyrstor controlled seres compensators (TCSC s) are used as FACTS devces. Ref.[8] presents a comprehensve survey on applcaton of varous conventonal, optmzaton and artfcal ntellgence (AI) based computatonal technques for mpact assessment of optmally placed and coordnated control of dstrbuted generatons (DGs) and FACTS controllers n power systems (Ss). In [9], a strategy for placement and szng of shunt FACTS controller usng Fuzzy logc and Real Coded Genetc Algorthm s proposed. A fuzzy performance ndex based on dstance to saddle node bfurcaton, voltage profle and capacty of shunt FACTS controller s proposed. In [10], optmal placement of parallel and seres FACTS devces s studed. The STATCOM s selected as a parallel FACTS devce and SSSC as a seres one. In order to fnd sutable FACTS locatons more easly and wth more flexblty, Ref.[11] presents a graphcal user nterface (GUI) based on a genetc algorthm (GA) whch s shown able to fnd the optmal locatons and szng parameters of mult-type FACTS devces n large power systems. Ref. [12] presents a novel FACTS placement method based on mxed nteger quadratcally constraned programmng (MIC). The method s capable of allocatng both SVC and Thyrstor-Controlled Seres Capactor (TCSC) ether separately or smultaneously, to determne concurrently ther numbers, locatons as well as szes. In ths paper, the cuckoo algorthm suggested for placement of UFC and SVC n power system. Ths paper has been organzed n fve sectons. Concept of obectve Internatonal Journal of Informaton, Securty and Systems Management, 2016,Vol.5,o.2, pp IJISSM

2 functon and modelng of UFC and SVC have been ntroduced n secton 2. The proposed technque to solve the problem are dscussed n thrd secton. Smulaton results are avalable n secton 4. Ths work has been concluded n secton FORMULATIO 2.1 Obectve functon In ths paper, obectve functon has been formulated to mprove dynamc behavor of power system. To do ths, obectve functon of ths study has been ntroduced as followng: VB VA OF = + + LA + G bus + FACTS bus + FACTS (1) mn max, = 1,..., (6) where, V mn and V max are the mnmum and maxmum voltages of bus, respectvely., mn and, max are the mnmum and maxmum voltages of bus, respectvely. s the number of buses of network. 2.3 Modelng FACTS devces SVC model Based on equatons of power flow of power system, the nected actve and reactve powers to bus are: = n V V = 1 G ( cos + θ B sn ) θ (7) where, bus: the number of system buses, FACTS: the number of FACTS devces, G: loss system, VB: voltage magntude devaton, VA: voltage angle devaton, LA: loadablty margn whch obtans as followng: SSL LA = (2) lne + FA CTS n = V V = 1 ( G snθ B cosθ ) where, B, G and θ obtan as: θ = θ θ (8) (9) where, lne and SSL are the number of lnes and the transmtted power of lne, respectvely. 2.2 Constrants To solve the obectve functon, four constrants should be observed. Equatons (3) and (4) show actve and reactve power flow constrants, respectvely. ( ) V VY cos δ δ θ = 0 g d = 1 ( ) V VY sn δ δ θ = 0 g d = 1 where, and are the number of recevng and sendng buses determned based on the sze of the used dstrbuton network. g and d are generated and consumed actve power n bus, respectvely. g and d are the generated and consumed reactve power n bus, respectvely. V and δ are system bus voltages magntudes and phase angles, respectvely. Y and θ are bus admttance matrx elements magntudes and phase angles, respectvely. Bus voltage and actve power flowed through buses should be lmted n reasonable ranges; ths fact has been llustrated n Equatons (5) and (6), (3) (4) G B = = Re Im ag al ( Y ) ( Y ) (10) (11) where, Y s system admttance matrx. SVC can be easly fxed to the reactve power nto the equaton (8) add busbar and the power nected n accordance wth the followng formula obtaned. n = 1 sh, = V V ( G snθ B cosθ ) + (12) where, sh, s reactve power capactor and reactor shunt s nstalled n the busbar. It should be noted that n the case of a negatve capactance value s sh UFC model UFC conssts of a seres voltage source and a parallel current source (see Fgure 1). Wth the provso that the sum of actve power nected by the seres voltage source and current source n parallel actve power nected nto the grd s zero [11]. V V V, = 1,..., mn max (5) 598

3 V V se <θ se V = +, UFC +, sh (25) y=g +b = +, UFC (26) I sh <θ sh = +, UFC (27), UFC =, SSSC, UFC =, SSSC, UFC =, SSSC, UFC =, SSSC = Fgure 1. Electrcal model of UFC +, UFC = +, UFC = +, UFC = +, UFC (13) (14) (15) (16) (17) (18) (19) (20) Shunt current source changes the nected power at th busbar. [ cos ] ˆ *, sh = V I sh = V I sh θ, sh + snθ S, sh, = V cos sh I sh θ, sh, = sn sh V I sh θ, sh (21) (22) (23) Based on these equaton,,sh s equal to,ufc thus the nected actve power of th busbar s same,sh and the nected reactve power at th busbar and the nected actve and reactve powers at th busbar change as followng; = +, sh (24) 3. OTIMIZATIO 3.1 Cuckoo optmzaton algorthm Based on Fgure2 n order to solve an optmzaton problem, t s necessary that the values of problem varables be formed as an array. In GA and SO termnologes ths array s called Chromosome and artcle oston, respectvely. But here n Cuckoo Optmzaton Algorthm (COA) t s called habtat. In a var-dmensonal optmzaton problem, a habtat s an array of 1 var, representng current lvng poston of cuckoo. Ths array s defned as follows: [ ] habtat = x, x,..., x 1 2 var (28) Each of the varable values (x1, x2,..., xvar) s floatng pont number. The proft of a habtat s obtaned by evaluaton of proft functon at a habtat of (x1, x2,..., xvar). So ( ) (,,..., ) roft = f habtat = f x x x (29) p p 1 2 var To start the optmzaton algorthm, a canddate habtat matrx of sze pop var s generated. Then some randomly produced number of eggs s supposed for each of these ntal cuckoo habtats. In nature, each cuckoo lays from 5 to 20 eggs. These values are used as the upper and lower lmts of egg dedcaton to each cuckoo at dfferent teratons. Another habt of real cuckoos s that they lay eggs wthn a maxmum dstance from ther habtat. From now on, ths maxmum range wll be called Egg Layng Radus (ELR). In an optmzaton problem wth upper lmt of var h and lower lmt of var low for varables, each cuckoo has an egg layng radus (ELR) whch s proportonal to the total number of eggs, number of current cuckoo s eggs and also varable lmts of var h and var low. So ELR s defned as: CE α ( var var ) ELR = h tot low (30) where, α s an nteger, supposed to handle the maxmum value of ELR. CE and tot are the total number of eggs avalable and the total number of eggs, respectvely. Another nterestng pont about lad cuckoo eggs s that 599

4 only one egg n a nest has the chance to grow. Ths s because when cuckoo egg hatches and the chcks come out, she throws the host brd s own eggs out of the nest. In case that host brd s eggs hatch earler and cuckoo egg hatches later, cuckoo s chck eats most of the food host brd brngs to the nest (because of her 3 tmes bgger body, she pushes other chcks and eats more). After couple of days the host brd s own chcks de from hunger and only cuckoo chck remans n the nest. IJISSM, 2016,Volume5,umber2 ( ) X = X + F X + X exthabtat CurrentHabtat Globalo nt CurrentHabtat (31) When young cuckoos grow and become mature, they lve n ther own area and socety for sometme. But when the tme for egg layng approaches they mmgrate to and better habtats wth more smlarty of eggs to host brds and also wth more food for youngsters. After the cuckoo groups are formed n dfferent areas, the socety wth best proft value s selected as the goal pont for other cuckoos to mmgrate. When mature cuckoos lve n all over the envronment t s dffcult to recognze whch cuckoo belongs to whch group. To solve ths problem, the groupng of cuckoos s done wth K-means clusterng method (a k of 3 5 seems to be suffcent n smulatons). ow that the cuckoo groups are consttuted ther mean proft value s calculated. Then the maxmum value of these mean profts determnes the goal group and consequently that group s best habtat s the destnaton habtat for mmgrant cuckoos. Fgure 2. Flowchart of cuckoo algorthm 3.2 Solvng problem After the ntroducton of the obectve functon parameters and algorthm, how to encode ths ssue must be expressed n the MATLAB envronment. Hence, the 9-step problem solvng presented n the followng locaton FACTS devces placed Tuesday. Step 1. Logn Informaton: In the frst step should, be provded nformaton needed to run the program. In general, three categores of nformaton requred, ncludng equpment nformaton, network and algorthms. Informaton Equpment. Informaton etwork (resstance and reactance lne, the coeffcent of tap changer, number of nodes, bus actve power, reactve power and the bass lne) All algorthms (most frequently, the number of repettons, number and rate Cuckoo) Step 2. Create a matrx sectons: the frst column matrx has two columns and column number two and number of sectons along the sectons where the last number s supposed FACTS system nstalled n the frst column sectons to Secton been nstalled. Step 3. Creatng random matrx: at ths stage, to mplement a genetc algorthm random matrx wth the sze of the ntal populaton sze and number of parameters s generated. Step 4. Runnng load flow program: load flow program 600

5 to determne parameters such as flow and can be mplemented. Step 5. Runnng the algorthm Cuckoo: Cuckoo operators algorthm s appled on the exstng generatons to be better solutons. Step 6. Check the devces: at ths stage, by creatng a matrx to dentfy sub power as well as power plants and the recognton of them, accordng to the avalable nformaton the selecton of equpment s done. Step 7. Check the values of the obectve functon parameters: value parameters n complance wth the provsons ade come to be evaluated. Step 8. Check satsfy the number of repeat and return to step 4 n case of non-fulfllment. Step 9. Derve optmal output. Fgure 4. Voltage devaton of system Accordng to Fgure 4, the presence of two UFC n the placement of ust UFC gve the best possble answer. Whle the placement of SVC, the presence of only a sngle SVC offer lowest voltage devaton. In smultaneous placement of two UFCs and one SVC has the most deal possble answers. 4.2 Voltage angle The voltage angle s the second parameter whch examned and presented n Fgure (5). Fgure bus test system Sngle lne dagram 4. CASE STUDY In ths secton, smulaton s done on 30-bus network. Sngle-lne dagram of ths test network s provded n Fgure 3. Several cases have been suggested based on the number of equpment. The maxmum number of equpment s sx. Each placement case by a two-dgt number shown. The frst number on the left sde of UFC and the number of SVC s second from left. 4.1 Voltage devaton The frst studed parameter s voltage devaton n the network. Fgure 4 shows voltage devaton n the network. Fgure 5. Voltage angle of system Based on the fgure (5), n placement ust UFC, voltage angle value by ncreasng the number of UFC has taken a downward turn and the best response n the presence of sx UFC sure the screw. But the SVCs placement wth, ths trend s reversed and ncreasng the number of SVC has opposte effect on voltage mpact angle and destroys t. Ths contradctory behavor n the placement of SVC and UFC s remarkable but the presence of three UFCs and one SVC has offer better soluton respect other cases. 4.3 Loadablty margn In fgure (6), loadablty margn of system s vsble n all cases. 601

6 Fgure 6. Loadablty margn of system Accordng Fgure (6) we can say that reducton of three placements of ust UFC, ust SVC and smultaneous SVC-UFC are Almost smlar and declnng. Accordng to ths placement of sx UFCs and fve SVCs (wth mnor dfference respect to sx SVCs), the best soluton are present for placements of ust UFC and ust SVC, respectvely. lacement of smultaneous three UFCs and three SVCs are the most acceptable answer among cases of smultaneous placement. Fgure 7. ower loss of system Fgure 8. Obectve functon of system Value of obectve functon has a drect and sgnfcant correlaton wth the number of equpment. The most of the equpment means the best answer. Therefore the presence of sx UFCs and sx SVCs as well as three SVCsthree UFCs generate the best solutons for placements of ust UFC, ust SVC and smultaneous, respectvely. 4.6 Sze and locaton UFC The locaton of UFC and ts nected voltage have been lsted n Table 1. Based on Table 1, lnes 6-8 and 3-4 have the maxmum number of UFC. Table (2), shows values of the nected reactve power of UFC. Based on Table 2, 0.58 pu s the maxmum value for reactve power and 0.01 pu s the mnmum value for the nected reactve power by UFC SVC As you can see, the Table (3) the locaton and capacty of the SVC and the amount of reactve power have been lsted. 4.4 ower loss ower loss s other parameter whch has been analyzed n ths study and ther results llustrated n fgure (7). Based on fgure (7), the presence of fve UFCs n UFC placement has best answer respect to other cases. The presence of four SVCs present lower power loss n placement of ust SVCs. Fnally n smultaneous placement of UFC and SVC, the presence of three UFCs and two SVCs have the best soluton. 4.5 Obectve functon Dscusson about of the obectve functon of ths study s the last case. Fgure 8 shows obectve functon of ths paper. 602

7 Table 1. Locaton and nected voltage by UFC The number Locaton (voltage) ( ) ( ), 2-1( ) 30-6( ), 6-2( ), 16-12( ) ( ), 25-24( ), 3-1( ), 2-6( ) ( ), 25-24( ), 9-11( ), 1-3( ), 28-8( ) ( ), 9-11( ), 24-25( ), 10-6( ), 12-4( ), 11-9( ) ( ) ( ) ( ) ( ), 1-2( ) ( ), 17-16( ) ( ), 10-6( ) ( ), 15-14( ), 6-2( ) ( ), 19-18( ), 4-3( ) ( ), 2-6( ), 10-6( ) Table 2. Locaton and nected reactve power by UFC The number Locaton (voltage) (0.50) (0.23), 2-1(0.33) (0.17), 6-2(0.24), 16-12(0.01) (0.32), 25-24(0.05), 3-1(0.03), 2-6(0.26) (0.26), 25-24(0.04), 9-11(0.08), 1-3(0.45), 28-8(0.11) (0.25), 9-11(0.06), 24-25(0.11), 10-6(0.28), 12-4(0.28), 11-9(0.18) (0.58) (0.47) (0.45) (0.09), 1-2(0.12) (0.14), 17-16(0.10) (0.48), 10-6(0.47) (0.44), 15-14(0.20), 6-2(0.37) (0.38), 19-18(0.06), 4-3(0.46) (0.17), 2-6(0.45), 10-6(0.11) 603

8 5. COCLUSIO The capablty and hghlghts of ths paper are: roblem formulaton: Obectve functon of each work determnes vson and drecton. In problem formulaton, all man challenges of power system have been consdered;.e. power loss, voltage profle and loadablty margn. Soluton technque: The cuckoo algorthm s one of the est ntellgent algorthms whch used to solve power system problems. Ths context, n addton to system study, capablty of the algorthm has analyzed. Case study: Smulaton has been performed n IEEE standard test system. In the system, power loss, devaton and angle of voltage, the nstalled capacty, loadablty and obectve functon have been presented n curves and tables. The number of parameters, t can lead to a comprehensve vew of the locaton of the FACTS devces. Table 3. Locaton and nected reactve power by SVC The number Locaton (reactve power) 01 8(0.56) 02 8(0.63) 13(0.38) 03 2(0.59) 13(0.43) 8(0.61) 04 5(0.59) 2(0.65) 4(0.21) 8(0.54) 05 12(0.29) 5(0.66) 8(0.69) 13(0.11) 2(0.59) 06 8(0.59) 5(0.45) 19(0.06) 12(0.36) 2(0.46) 5(0.17) 11 8(0.58) 12 8(0.63) 12(0.21) 13 8(0.62) 13(0.63) 17(0.09) 21 8(0.51) 22 8(0.62) 24(0.11) 23 16(0.11) 13(0.60) 21(0.10) 31 8(0.48) 32 5(0.54) 8(0.61) 33 8(0.44) 13(0.40) 5(0.55) REFERECE 1) Hngoran. G., & Gyugy L. (1999). Understandng FACTS: Concepts and Technology of Flexble AC Transmsson Systems, Wley-IEEE ress. 2) Zhang X.-., Rehtanz Ch., & al B. (2006). Flexble AC Transmsson Systems: Modellng and Control. Sprnger. 3) Srnvasa Rao R., & Srnvasa Rao V. (2015). A generalzed approach for determnaton of optmal locaton and performance analyss of FACTs devces, Electrcal ower and Energy Systems. vol. 73, pp ) Balamurugan K., Muralsachthanandam R., & Dharmalngam V. (2015). erformance comparson of evolutonary programmng and dfferental evoluton approaches for socal welfare maxmzaton by placement of mult type FACTS devces n pool electrcty market, Electrcal ower and Energy Systems. vol. 67, pp ) Rezaee Jordeh A. (2015). artcle swarm optmsaton (SO) for allocaton of FACTS devces n electrc transmsson systems: A revew, Reable and Sustanable Energy Revews. vol. 52, pp ) Bhattacharyya B., & Kumar S. (2015). Reactve power plannng wth FACTS devces usng gravtatonal search algorthm, An Shams Engneerng Journal. vol. 6, pp ) Rezaee Jordeh A. (2015). Branstorm optmsaton algorthm (BSOA): An effcent algorthm for fndng optmal locaton and settng of FACTS devces n electrc power systems, Electrcal ower and Energy Systems. vol. 69, ) Sngh B., Mukheree V., & Twar. (2015). A survey on mpact assessment of DG and FACTS controllers n power systems, Reable and Sustanable Energy Revews. vol. 42, pp ) hadke A.R., Fozdar M., & az K.R. (2012). A mult-obectve fuzzy-ga formulaton for optmal placement and szng of shunt FACTS controller, Electrcal ower and Energy Systems. vol. 40, pp ) Rahmzadeh S., & Tavakol Bna M. (2011). Lookng for optmal number and placement of FACTS devces to manage the transmsson congeston, Energy Converson and Management. vol. 52, pp ) Ghahreman E., & Kamwa I. (2013). Optmal lacement of Multple-Type FACTS Devces to Maxmze ower System Loadablty Usng a Generc Graphcal User Interface, IEEE Transactons on ower Systems, vol. 28, no.2, pp ) Chang R.W., & Saha T.K. (2014). A novel MIC method for FACTS allocaton n complex real-world grds, Electrcal ower and Energy Systems. vol. 62, pp