OPTIMAL PLACEMENT OF TCSC USING WIPSO

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1 OPTIMAL PLACEMENT OF USING IPSO K. Kavia and R. Neea Department of Eectrica Engineering, Annamaai University Chambaram, Tami Nadu, India E-Mai: ABSTRACT Modern power systems are heaviy oaded and are being operated in ways not originay envisioned. Fexibe AC Transmission System (FACTS) devices pay a vita roe in improving e static as we as dynamic performance of e power systems. FACTS devices are muti-functiona contro devices which can be used to effectivey contro e power distribution and e power transfer capabiity, to reduce active power osses, to improve stabiities of e power network, to decrease e cost of power production and to fufi e oer contro requirements by controing e power fow in e network. FACTS devices are based on so-state contro and so are capabe of contro actions at far higher speed. However e ocation and rating of e FACTS devices pay a major roe in decing e extent to which e objective of improving e system performance is achieved in a cost effective manner. In is work an objective function comprising of cost, ine oadings and oad votage deviations is proposed to tap imum benefits out of eir instaation and e weights assigned to em dece e reative importance. IPSO (eight Improved PSO) technique is appied for soving e probem and e effectiveness of e proposed meod is tested on IEEE 14, 30 and 57 bus systems using MATAB. The resuts of e proposed technique are compared wi e resuts obtained rough e appication of PSO agorim for e same objective function. Keywords: FACTS devices, yristor controed series capacitor (), partice swarm optimization (PSO),weight improved PSO (IPSO), security enhancement. INTRODUCTION The secure and reiabe operation of power systems has become an important issue in today s arge and highy compex interconnected systems. So it becomes essentia to improve e eectric power utiization whie maintaining e reiabiity and security. Due to increase in oad demand, e magnitude of e power fows in some of e transmission ines are we above eir norma imits and in some oer ines, it is beow eir norma. As a resut of is uneven oad distribution e votage profie of e system gets deteriorated which poses a reat for e security of e system. Consering e factors such as ever increasing oad demand, economica and technica constraints invoved in setting up new power generation faciities and imitations faced in purchasing right of ways to reaize new transmission corrors, it becomes highy essentia to utiize e existing generation and transmission faciities in e most efficient manner. To achieve is, FACTS controers are found to be an effective aternative for e compex task of buiding up new transmission corrors [1]. FACTS (Fexibe Aternating Current Transmission System) is a concept introduced by N.G.Hingorani [2]. FACTS devices are muti-functiona contro devices which can be used to effectivey contro e power distribution and e power transfer capabiity, to improve e votage profie, to reduce ine oadings, to reduce active power osses, to prove reactive power support, to improve stabiities of e power network and aso to decrease e cost of power production. Controing e power fow in an eectric power system wiout generation rescheduing or topoogica changes can improve e performance of system conseraby [3]. To achieve e imum benefits rough e instaation of FACTS devices, it is highy important to determine e optima ocation and suitabe ratings of e devices in e power system [4]. (Thyristor Controed Series Capacitor) is a type of series compensator at can prove many benefits for a power system incuding contro of power fow in e ine damping power osciations and mitigating sub synchronous resonance [5]. is a variabe impedance type series compensator. It consists of a series compensating capacitor shunted by a yristor controed reactor. By controing e firing ange of yristor, can change e ine reactance smooy and rapy. has one of e two possibe characteristics eier capacitive or inductive, ereby increasing or decreasing e reactance of e ine X [6]. Moreover to avo e over compensation of e ine, e imum vaues of capacitance and inductance are fixed at 0.8X and 0.2X [7]. ord s first 3 phase, 2*165 MAR, was instaed in 1992 in Kayenta substation, Arizona. It raised e transmission capacity of transmission ine by 30% and effectivey damped eectromechanica power osciations [8]. Optima pacement of is essentia to tap e imum benefits in terms of system performance and cost effectiveness. A oss sensitivity index wi respect to e contro parameters of FACTS devices has been suggested 9159

2 and wi e computed oss sensitivity index, e FACTS devices are paced on e most sensitive bus or ine [9]. Fuzzy based approach for e optima pacement of FACTS device for enhancing e system security under norma and network contingencies has been discussed in [10]. The optima ocation of a given number of FACTS devices is a probem of combinatoria anaysis. To sove such kind of probems, heuristic meods can be used [11]. They permit to obtain acceptabe soutions wiin a imited computation time. The appication of Genetic Agorim for e optima ocation of muti type FACTS devices in order to imize e system oadabiity is anaysed in [12]. A Differentia Evoution based agorim to dece e optima ocation and device rating has been suggested in [13] wi an objective of enhancing e system security under singe ine contingencies. The Partice Swarm Optimization (PSO) is appied for e optima ocation of FACTS devices to achieve minimum cost of instaation and to improve system oadabiity, by consering erma imit for e ines and bus votage imit for e oad buses as constraints [14]. Sensitivity anaysis approach for finding e optima ocation and PSO for e optima parameter setting of has been suggested in [15] so as to imize e oadabiity. A nove eight Improved PSO meod based on e improved function of weight parameter is appied for soving Economic oad dispatch probems [16]. As bus votage deviations on e oad buses are aso consered as appropriate indices at refect e system security, ese votage deviations aong wi e cost of instaation of e devices and e ine oading are used to formuate e objective function at is to be minimized and e IPSO technique is used to sove e probem in is work. PROBLEM FORMULATION Objective of e optimization As e cost of e FACTS devices, especiay is high, in order to achieve e imum benefit, e devices are to be instaed at e optima ocations. Minimizing e cost of instaation of e is chosen as e primary objective and e objective function is augmented wi two indices, one for e oad votage deviation and e oer for ine oading ereby making it a comprehensive one, whose minimization eads to a cost effective, security oriented soution. The objective function is formuated as C S LD LL MinF 1 * 2 3 (1) F is e objective function; C = Cost of device in US $/Kar; S = Operating range of ; LD = Load votage deviation; LL = Line oading;, 1 2 and 3 are e weight factors. (i) Cost ( C ) The first term of e objective function C presents e instaation cost of device in e network, which is given by e foowing equation. 2 C s s (2) (ii) Load votage deviation LD Excessive high or ow votages can ead to an unacceptabe service quaity and can create votage instabiity probems. s connected at appropriate ocations pay a eading roe in improving votage profie ereby avoing votage coapse in e power system. The second term represents e oad votage deviations in order to avo e unsatisfactory votage profies on oad buses. LD nb m1 mref mref m n m = otage magnitude at bus m mref = Nomina votage at bus m and is consered as 1.0 pu. m = Load buses, where m is ess an mref. (iii) Line oading LL is ocated in order to remove e overoads and to distribute e oad fows uniformy. To achieve is, ine oading is consered as e ird term in e objective function. LL n S S 1 n S = Apparent power in e ine. S = Apparent power rating of ine. The optimization variabes (3) (4) 9160

3 The optimization variabes consered in is work are (a) The number of devices to be instaed is taken as e first variabe. (b) ocation is consered as e second variabe to be optimized. s are not instaed in e ines where e transformers exist. (c) The reactance of e is consered as e ird variabe. Modeing of device is a series compensator. It consists of a series compensating capacitor shunted by a yristor controed reactor as shown in Figure-1.i e power fow contro can be achieved by varying e overa ines effective series transmission impedance. The is modeed as a variabe reactance as shown in Figure-2. partices. In each ation, G of e current swam is compared wi eg of e previous ation and whichever is ower is retained aong wi e corresponding partice. The position update of partices is carried out rough e expression (6) and e veocity is cacuated using (7). X X (6) k 1 k k 1 K1 c r K 1 1 k k k k P X c r G X 2 2 The inertia weight in (7) is cacuated using e foowing expression. (7) min * (8) Figure-1. Figure-2. This ative procedure is repeated ti a specified number of swarm are generated or unti a predefined amount of time has eapsed or unti ere is no conserabe difference between e outcomes of any two subsequent ations. The working range of is consered as foows. 0.8X X 0. 2X (5) X is e reactance added to e ine by pacing. X is e overa ine reactance. Overview of PSO technique Partice swarm optimization is a heuristic search technique deveoped by Eberhart and Kennedy [16] based on e concept of swarm inteigence exhibited by e fock of birds, schoo of fish etc in which each member of e group adjusts its behavior based upon its own experience and e experience of e swarm..this sort of socia behavior is used to simuate e probem soving environment in which a swarm is randomy generated in terms of soution variabes of e probem. The indivuas in a swarm are caed partices. After generating e swarm, e fitness vaues of e partices P are evauated and compared against e vaues obtained from e previous ation. The partices wi e vaues of fitness function in e next generation P are retained. G is e vaue attained so far by e swarm of k 1 = eocity of e ation. k = eocity of e k X = Position of e i indivua at k 1 i indivua at i indivua at k 1 X = Position of e ation. P G = Best position of e k ation k ation. i indivua at k 1 i indivua. = Best position among e indivuas. r 1, r2 = Random numbers distributed wiin e interva [0, 1] c 1 = Cognitive factor c 2 = Socia factor = Inertia weight =Initia vaue of inertia weight min = Fina vaue of inertia weight = Maximum number of ations = Current ation number 9161

4 d 1,2,...D, D is e number of members in a partice. i 1,2,...m, m is e size of e swarm. IPSO-eight improved PSO technique IPSO is based on e improved weight parameter function. For getting e better goba soution, e traditiona PSO agorim is improved by adjusting e inertia weight, cognitive and socia factors. The veocity of an indivua i of IPSO is given by K1 here, new K new cr k k k k P X c r G X 1 1 min r 3 min 2 2 (9) (10) (11) c1 c1min c1 c1 (12) c2 c2 min c2 c2 (13) r 3 = Random number distributed wiin e interva [0, 1] = Initia vaue of inertia weight. min = Fina vaue of inertia weight. c = Initia vaue of cognitive factor. c 1min 1 = Fina vaue of cognitive factor. c 2 min = Initia vaue of socia factor. c = Fina vaue of socia factor. 2 = Maximum number of ations. = Current ation number. 5. ALGORITHM The agorim of e proposed work is expained beow. Step 1: The system data and e oad factor are initiaized. Step 2: IPSO parameters such as e size of swarm m, e number of variabes to be optimized, imits of each variabe in e partice, c 1 and c 2, C 1, C 2 min veocity imits, C 2 and P and G are initiaized. C 1 min and, D,,, min and Step 3: An initia popuation is randomy generated consering e variabes to be optimized. [The number of s, ocation of, parameter setting of ] Step 4: For each partice i [i = 1, 2,..m] in e popuation, e objective function is evauated. Step 5: The objective function vaue of each partice is compared wi e corresponding P of previous ation and P of each partice is updated. Step 6: G is entified, en compared wi e G in e previous ation and it is updated. Step 7: A new popuation is created by updating e veocity and position of e partice. Step 8: If stopping crion is satisfied, e indivua is entified; ese steps from 4 are repeated. Step 9: The steps from 2 to 8 are repeated for different oad factors. SIMULATED RESULTS The proposed meod has been tested on standard IEEE 14 bus, 30 bus and 57 bus test systems. To study e effect of e instaation of on oad bus votages and ine oadings under overoad conditions, e oads on e system were increased in a step by step manner; e rea and reactive power oads connected at various oad buses were increased keeping e oad power factor constant. The imum number of FACTS devices is imited to 2 on a 14 bus system, 3 on a 30 bus system and 5 on a 57 bus system. The Tabes 1, 5 and 9 show e ocation of s for 14, 30 and 57 bus systems obtained using PSO agorim. Simiary e Tabes 2, 6 and 10 show e ocation of s for 14, 30 and 57 bus systems obtained using IPSO agorim. The Tabes 3, 7 and 11 show variation in ine oading before and after e pacement of using PSO and IPSO agorims for 14, 30 and 57 bus systems. The Tabes 4, 8 and 12 show variation in oad votage deviation before and after e pacement of using PSO and IPSO agorims for 14, 30 and 57 bus systems. From e tabuated resuts, it has been observed at ere is a significant reduction in e ine oadings and oad votage deviations when s are connected at optima ocations using IPSO agorim. 9162

5 A. Case I: 14 bus system Tabe-4. Load votage deviation for different oad factors. PSO Tabe-1. ocation. IPSO Tabe-2. ocation. Tabe-3. Line oading for different oad factors. Figure-4. Load votage deviation s percentage of oad. B. Case II: 30 bus system PSO Tabe-5. ocation. IPSO Tabe-6. ocation. Figure-3. Line oading s percentage of oad. 9163

6 Tabe-7. Line oading for different oad factors. Case III: 57 bus system PSO Tabe-9. ocation. IPSO Tabe-10. ocation. Figure-5. Line oading s percentage of oad. Tabe-8. Load votage deviation for different oad factors. Tabe-11. Line oading for different oad factors. Figure-6. Load votage deviation s percentage of oad. Figure-7. Line oading s percentage of oad. 9164

7 Tabe-12. Load votage deviation for different oad factors. systems. Though s can be paced at any feasibe ocation in e power system, eir ocations and ratings are to be fixed optimay as ey turn out to be costier an e conventiona compensating devices. Here e probem of device pacement is gued rough e IPSO agorim which gives e soution for e comprehensive objective function consisting of cost of e device, oad votage deviations and ine oadings. The proposed meod yieds an efficient soution when compared to PSO and conseraby reduces oad votage deviations and reieve e ines off eir over oads under various oad conditions. REFERENCES [1] Surekha Manoj, Dr. Puttaswamy P.S Importance of FACTS Controers in power systems. Internationa Journa of Advanced Engineering Technoogy. II(III): [2] N. G. Hingorani and L. Gyugyi Understanding FACTS. IEEE Press. Figure-8. Load votage deviation s percentage of oad. As ine oadings form a part of e objective function, overoad conditions were simuated by simutaneousy varying e rea and reactive powers on e oad buses keeping e oad power factor constant. Minimizing e instaation cost of e devices forms e oer objective and is is achieved by optimay sizing and pacing e devices. Under over oaded conditions, e increased votage drops on transmission ines resut in unsatisfactory votage profie. So, reduction in e deviation of oad votages has been made as a ird objective. Line oadings and oad votage deviations under different oad conditions are shown from Figure 3 8 for 14, 30 and 57 bus systems. These variations are shown in e form of bar charts for ree different cases namey when e system is operated wiout and when e device pacement is carried out rough PSO and IPSO techniques. It has been observed at ine oadings and e oad votage deviations are reduced when e system is operated wi s at optimum ocations. There is a significant improvement in e performance of e system when e device pacement is done rough IPSO agorim. CONCLUSIONS IPSO (eight Improved PSO) technique has been appied for e security enhancement of power [3] S. N. Singh, A. K. Dav Optima ocation of FACTS devices for congestion management. Eectric Power Systems Research. 58: [4] R.Benab, M.Boudour, M.A. Abo Optima ocation and setting of SC and devices using non-dominated sorting Partice Swarm Optimization. Eectric Power Systems Research. 79: [5] Hugo Ambriz-Perez, Enrique Acha, Caudio R. Fuerte- Esquive firing ange mode for optima power fow soutions using Newton s meod. Eectric Power Systems Research. 28: [6] NareshAcharya, N.Miuanaan Locating series FACTS devices for congestion management in dereguated eectricity markets. Eectric Power Systems Research. 77: [7] Ghamgeen I.Rashed, Yuanzhang Sun, Kai-Pei Liu Optima pacement of Thyristor Controed Series Compensation in Power System based on Differentia Evoution Agorim. IEEE, seven Internationa Conference on Natura Computation. [8] S.Meikandasivam, Rajesh Kumar Nema, Shaiendra Kumar Jain Behaviora Study of device- A matab / simuink Impementation. ord Academy of Science, Engineering and Technoogy. 9165

8 [9] PreechaPreedavichit, S.C. Srivastava Optima reactive power dispatch consering FACTS devices. Eectric Power Systems Research. 46: [10] K.isaka, D.Thukaram, Lawrence Jenkins Appication of UPFC for system security improvement under norma and network contingencies. Eectric power systems research [11] Sung-Hwan Song, Jung-Uk Lim, Seung-II Moon Instaation and operation of FACTS devices for enhancing steady-state security. Eectric Power Systems Research. 70: [12] StephaneGerbex, RachCherkaoui, Aain J. Germond Optima ocation of muti-type FACTS devices in a power system by means of genetic agorims. IEEE Transactions on power systems. o. 16 [13] Husam I.Shaheen, Ghamgeen I.Rashed, S.J. Cheng Optima ocation and parameter setting of UPFC for enhancing power system security based on differentia evoution agorim. Eectric Power Systems Research. 33: [14] M.Saravanan, S.Mary Raja Sochana, P.enkatesh, J.Prince Stephen Abraham Appication of partice swarm optimization technique for optima ocation of FACTS devices consering cost of instaation and system oadabiity. Eectric Power Systems Research. 77: [15] K.Satyanarayana, B.K..Prasad, G.Devanand, N.Siva Prasad Optima ocation of wi minimum instaation cost using PSO. Internationa Journa of computer science and technoogy. o. 2 [16] James Kennedy, Russe Eberhart Partice Swarm Optimization. In: Proceedings of IEEE Internationa Conference on Neura Networks. 14: [17] PhanTu u, DinhLuong Le, NgocDieuo, Josef Tusty A Nove eight-improved Partice Swarm Optimization Agorim for Optima Power Fow and Economic Load Dispatch Probems. Transmission and distribution conference and Exposition, IEEEPES. pp