1&:It!J Proceedings of the "International conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET20/3)


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1 1&:It!J Proceedings of the "International conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET20/3) L = t:i "" organized by Sathyabama University, Chennai, India in association with DRDO, New Delhi, India, 24 1h _261 h, July, 2013 Self Adaptive Firefly Algorithm Based Transmission Loss Minimization using TCSC RSelvarasu #l, CChristober Asir Rajan * 2 " Department ofeee, JNTUH, Hydera bad, India * Department of EEE, Pondicherry Engineering College, Puducherry, India I INTRODUCTION In recent years, the demand for electrical energy is exponentially increasing The construction of new generation system, power transmission networks can solve these demands However, there are some limitations to construct new system They involve installation cost, environment impact, political, large displacement of population and land acquisition One of the alternative solutions to respond the increasing demand is by minimization of transmission loss using Flexible Alternating Current Transmission Systems (FACTS) devices The FACTS is a concept proposed by NGHingorani [1] as a well known term for higher controllability in power system by means of power electronic devices Better utilization of an existing power system capacity by installing FACTS devices has become essential in the area of ongoing research FACTS devices have the capability to control the various electrical parameters in transmission network in order to achieve better system performance F ACTS devices can be divided in to three categories Shunt Connected, Series connected and combination of both [2] The Static Var Compensator (SVC) and Static Synchronous Compensator (ST A TCOM) are belongs the shunt connected devices and are in use for a long time in various places Consequently, they are variable shunt reactors which inject or absorb reactive power in order to control the voltage at a given bus [3]Both Thyristor Controlled Series Compensator (TCSC) and Static Synchronous Series Compensator (SSSC) are series connected devices The TCSC and SSSC mainly control the active power in a line by varying the line reactance They are in operation at a few places but are still in the stage of development [45]Unified Power Flow Controller (UPFC) is belongs to Combination of Shunt and Series devices UPFC is able to control active power, reactive power and voltage magnitude simultaneously or separately [6] Optimal location of various types of FACTS devices in the power system has been experienced using different Metaheuristic algorithm such as Genetic Algorithm (GA),Simulated 2asir AbstractThis paper presents the use of Thyristor annealing (SA),Ant Colony Optimization (ACO), Bees Controlled Series Compensator (TCSC) to minimize the algorithms (BA), Differential Evolution (DE), and Particle transmission loss in power system network SelfAdaptive Swarm Optimization (PSO) etc [7]Optimal location of multi Firefly Algorithm is proposed to identify the optimal type FACTs devices in a power system to improve the location and parameter of TCSC To validate the loadability by means of Genetic Algorithm has been proposed algorithm, simulations are performed on IEEE successfully implemented [9]PSO has been applied to 14bus and IEEE 30bus system using MATLAB software determine the optimal location of FACTS devices, reactive package Analyzing the simulation results the identified power and voltage control [1011] PSO has been proposed to location and parameter of TCSC is able to minimize the improve the power system stability by determining the optimal transmission loss in the power system network location and controller design of STATCOM [12] PSO has KeywordsFirefly Algorithm, Loss Minimization, been proposed to select the optimal location and setting Optimal Location, TCSC parameter of SVC and TCSC to mitigate small signal oscillations in multi machine power system [13] Bacterial Foraging algorithm has been used to find the optimal location of UPFC devices with objectives of minimizing the losses and improving the voltage profile [14] Firefly Algorithm has been developed by XinShe Yang [78] which is found superior over other algorithms in the sense that it could handle multi modal problems of combinational and numerical optimization more naturally and efficiently [15] It has been then applied by various researchers for solving various problems, to name a few: economic dispatch [1617], fault identification [18], crypto analysis of knapsack problems [19], scheduling [20], Unit commitment [21] and image compression [22] etc In this paper Self Adaptive Firefly Algorithm is proposed to identify the optimal location and parameter of TCSC, which minimizes the transmission loss in the power system network Simulations are performed on IEEE 14bus and IEEE 30bus system using MA TLAB software package Simulations results are presented to demonstrate the effectiveness of the proposed approach II TCSC MODEL The Thyristor Controlled Series Compensator (TCSC) is capacitive reactance compensator, which consists of a series capacitor bank shunted by a thyristor controlled reactor in order to provide a smoothly variable series capacitive reactance [2] The TCSC can be connected in series with the transmission line to compensate the inductive reactance of the transmission line The reactance of the TCSC depends on its compensation ratio and the reactance of the transmission line where it is located The TCSC model is shown in Figl The TCSC modeled by the reactance, Xn:sc: as follows, X ij = Xline + Xrcsc (1) (2) /13/$ IEEE 683
2 I Proceedings of the "International Conference on Advanced Nanomaterials & tr Emerging Engineering ech logies" (ICANMEET20J3) organized by Sathyabama Umverslty, Chennal, IndIa III association with DRDO, New Deihl, IndIa, 2426, July, 2013 La III;:J where Xline is the reactance of the transmission line and rrcsc is the compensation factor of the TCSC specific optimization problem However, typically a population size of 20 to 50 is used for PSO and Firefly Algorithm for most applications [10, 25] Each ni h firefly is denoted by a vector (3) FigITCSC Model III FIREFLY ALGORITHM A Classical Firefly Algorithm Firefly Algorithm is a recent nature inspired meta heuristic algorithms which has been developed by Xin She Yang at Cambridge university in 2007[7] The algorithm mimics the flashing behavior of fireflies It is similar to other optimization algorithms employing swarm intelligence such as PSO But Firefly Algorithm is found to have superior performance in many cases [8]1t employs three ideal rules First, all fireflies are unisex which means that one firefly will be attracted to other fireflies regardless of their sex Secondly, the degree of the attractiveness of a firefly is proportional to its brightness, thus for any two flashing fireflies, the less bright one will move towards the brighter one More brightness means less distance between two fireflies However if any two flashing fireflies are having same brightness, then they move randomly Finally the brightness of a firefly is determined by the value of the objective function For a maximization problem, the brightness of each firefly is proportional to the value of the objective function In case of minimization problem, the brightness of each firefly is inversely proportional to the value of objective function Firefly Algorithm initially produces a swarm of fireflies located randomly in the search space The initial distribution is usually produced from a uniform random distribution The position of each firefly in the search space represents a potential solution of the optimization problem The dimension of the search space is equal to the number of optimizing parameters in the given problem The fitness function takes the position of a firefly as input and produces a single numerical output value denoting how good the potential solution is A fitness value is assigned to each firefly The brightness of each firefly depends on the fitness value of that firefly Each firefly is attracted by the brightness of other fireflies and tries to move towards them The velocity or the pull a firefly towards another firefly depends on the attractiveness The attractiveness depends on the relative distance between the fireflies It can be a function of the brightness of the fireflies as well A brighter firefly far away may not be as attractive as a less bright firefly that is closer In each iterative step, Firefly Algorithm computes the brightness and the relative attractiveness of each firefly Depending on these values, the positions of the fireflies are updated After a sufficient amount of iterations, all fireflies converge to the best possible position on the search space The number of fireflies in the swarm is known as the population size, N The selection of population size depends on the where nd the number of decision variables The search space is limited by the following inequality constraints xv(min) xv xv(max) v = 1,2,,nd (4) Initially, the positions of the fireflies are generated from a uniform distribution using the following equation Xll = XV (min) + (x v (max) x v (min) ) x rand (5) Here, rand is a random number between 0 and I, taken from a uniform distribution The initial distribution does not significantly affect the performance of the algorithm Each time the algorithm is executed, the optimization process starts with a different set of initial points However, in each case, the algorithm searches for the optimum solution In case of multiple possible sets of solutions, the algorithm may converge on different solutions each time But each of those solutions will be valid as they all will satisfy the requirements The light intensity of the ni h firefly, n is given by The attractiveness between ni h and n th firefly, f3 m, n by Pm,n = (Prm;x,m,n Pmin,m,n ) exp( rmrm,n 2) + Prnil,"m,n (7) where r lti,n is given is Cartesian distance between ni h and n th firefly The value of {J,nin is taken as 02 and the value of Pmax is taken as 1 r is another constant whose value is related to the dynamic range of the solution space The position of firefly is updated in each iterative step If the light intensity of n th firefly is larger than the light intensity of the ni h firefly, Jh th then the m firefly moves towards the n firefly and its motion at J( h iteration is denoted by the following equation: xm(k) = xm(kl) + It,n (xm(kl)xm(kl)) +a(ramo5) (8) (9) 684
3 I Proceedings of the "International Conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET20J3) organized by Sathyabama University, Chennai, India in association with DRDO, New Delhi, India, 24 t h _26tl" July, 2013 LiiII :J_ The random movement factor a is a constant whose value depends on the dynamic range of the solution space At each iterative step, the intensity and the attractiveness of each firefly is calculated The intensity of each firefly is compared with all other fireflies and the positions of the fireflies are updated using (9) After a sufficient number of iterations, all the fireflies converge to the same position in the search space and the global optimum is achieved B Self Adaptive Firefly Algorithm In the above narrated Firefly Algorithm, each firefly of the swarm travel around the problem space taking into account the results obtained by others, still applying its own randomized moves as well The random movement factor (a) is very effective on the performance of Firefly Algorithm A large value of a makes the movement to explore the solution through the distance search space and smaller value of a tends to facilitate local search In this paper the random movement factor (a) is dynamically tuned in each iteration The influence of other solutions is controlled by the value of attractiveness (7), which can be adjusted by modifying three parameters a, Pmin and rln general the value of Pmax should be used from 0 to I and two limiting cases can be defined: The algorithm performs cooperative local search with the brightest firefly strongly determining other fireflies positions, especially in its neighborhood, when /l,nax = I and only noncooperative distributed random search with Pmax = o On the other hand, the value of r determines the variation of attractiveness with increasing distance from communicated firefly Setting r as 0 corresponds to no variation or attractiveness is constant and conversely putting r as 00 results in attractiveness being close to zero which again is equivalent to the complete random search In general r in the range of 0 to I 0 can be chosen for better performance Indeed, the choice of these parameters affects the final solution and the convergence of the algorithm Each firefly with nd decision variables in the Firefly Algorithm will be defined to encompass nd +3Firefly Algorithm variables in a selfadaptive method, where the last three variables represent am ' Pminm and rm A firefly can be represented as (10) To achieve the better utilization of an existing power system, the optimal location and parameter of TCSC to be identified in the power transmission network to minimize the total real power loss The objective of this paper is to identify the optimal location and parameter of the TCSC which minimize the real power loss Rrrltll? POWY S)8lemIUa Sdedtll?[XpiatiOlsizeNan1Mlxinwnl1lltJierqIteratirn;jrrcrYM!llJ?lll?drr:k C rerute til? initid [XpictiOl Wile ernin:tioli'f!quirr:i11!n1sa"e ndm!l:) ch jrrm=i:n Alter til? ftemch!n, a, mn arl r a:rrrdirg to m/h jrrljlywiws!itn til? lax! flcm' Cmpde til? Red JXMf!t' lass Cak:uJde n Frr n=i:n Alter tll? ftemch!n=adirgto nthfirljlywiws!itn til? lax! flcm' Cmpde til? Red JXMf!t' lass Cak:uJde In If1m<ln Cmpute ':nj' U5irg (8) wzae /J",,, usirg(7j MJu? nl''firifly t(mltm rt jrrifly thuffz (51) en1if en1jrrn en1jrrm &irk til? jrrljlie5 an1jind til? CUffed!:eft FniWiJe Fni Fig2Psuedo Code of Firefly Algorithm A Objective function The objective is to minimize transmission loss, which can be evaluated from the power flow solution [14], and written as nl Min oss = L G/( 2 + V/  2 Vj cos 0ij) (11) 1=1 where oss =Real power loss, G1 =Conductance of rh line, =Voltage magnitude at bus i, =Voltage magnitude at bus j, S =Voltage angle at bus i and j and nl =Total If number of transmission lines Each firefly possessing the solution vector, am ' Pmin,m and rm undergo the whole search process During iteration, the FA produces better offsprings through (7) and (9) using the parameters available in the firefly of (10), thereby enhancing the convergence of the algorithm The basic steps of the Firefly Algorithm can be summarized as the pseudo code which is depicted in Fig 2 B Pro blem constraints The equality constraints are the load flow equation given by Pui  PDi = PJV, 0) (12) QUi  QDi = Q (V, 0) (13) where Pc;i and QUi represent the real and reactive power injected by the generator at bus i, respectively PDi IV PROBLEM FORMULATION 685
4 I Proceedings of the "International Conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET20J3) organized by Sathyabama University, Chennai, India in association with DRDO, New Delhi, India, 24th _26tl" July, 2013 LiiII :J_ and QJ)i represent the real and reactive power drawn by the load at bus i, respectively Two inequality constraints are considered as follows The first constraint includes reactive power generation at PV buses Q,ṃin Q, max Q, (14) (JI Cd e,f were h Q min Q max Gi and are the upper and lower limit of Gi reactive power source i The second constraint represents the rating of TCSC O8Xline XiCSC O2XlineP U (15) The Self Adaptive firefly algorithm based optimal location of TCSC problem is defined as xm ={, 'XC',{"l'l,ATin,m' rnj (LM' 'XC':'t"M'l,fJnin,m' rnj (LN' I1CY',N' ' fjrrin,n' YN)} (16) where LM is the Line location of the MhTCSC, Yrcsc,M is the Compensation factor of M h TCSC The Self Adaptive Firefly Algorithm searches for optimal solution by maximizing light intensity I m ' like fitness function in any other stochastic optimization techniques The light intensity function can be obtained by transforming the power loss function into I m function as 1 MaxIm =  (17) 1 + oss A population of firefly is randomly generated and their intensity is calculated using (6) Based on the light intensity, each firefly moved to the optimal solution through (9) and the iterative process continues till the algorithm converges V SIMULATION RESULTS AND DISCUSSIONS The simulation results for optimal location and parameter of Thyristor Controlled Series compensator to minimize the transmission loss in the power system network were obtained and discussed in this section The line data and bus data for the IEEE 14bus and IEEE 30bus are taken from [24] Case 1: IEEE 14 bus system In first case IEEE 14 bus system has been considered to identify the optimal location and parameter of the TCSC to minimize the real power loss IEEE 14 bus system has 20 transmission lines, five generator buses (bus no 1,2,3,6 and 8) and rests are load buses Simulations are carried out for different number of TCSC without considering the installing cost The simulation results in terms of the locations and the TCSC parameters and the resulting loss are presented in table I it is observed from the table I that the identified location of TCSC minimizes the real power loss When three TCSC is considered real power loss considerably reduced from MW to MW If four TCSC is considered the real power loss reduction is insignificant from the installation cost point of view It is thus concluded that three TCSC devices are adequate to achieve the desired goal of minimizing the loss TABLE I OPTIMAL LOCATION, PARAMETER of TCSC and REAL POWER LOSS for IEEE 14 BUS SYSTEM Real power Type of No of loss Locations YICSC System TCSC in MW (Line No) in pu IEEE bus TABLE II OPTIMAL LOCATION, PARAMETER of TCSC and REAL POWER LOSS Type of System for IEEE 30 BUS SYSTEM Real power No of Locations loss YTCSC TCSC (Line No) in MW in pu IEEE bus
5 I Proceedings of the "International Conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET2013) organized by Sathyabama University, Chennai, India in association with DRDO, New Delhi, India, 24 t h LiiII _26tl" July, 2013 :J_ Case 2: IEEE 30 bus system In this case IEEE 30 bus system has been considered to identify the optimal location and parameter of the TCSC to minimize the real power loss IEEE 30 bus system has 41 transmission lines, six generator buses (bus no I, 2, 5, 8, 11 and 13) and rests are load buses Simulations are carried out for different number of TCSC without considering the installing cost The simulation results in terms of the locations and the TCSC parameters and the resulting loss are presented in table II It is observed from the table II that the identified location of TCSC minimizes the real power loss When six TCSC is considered real power loss considerably reduced from MW to MW It is thus concluded that six TCSC devices are adequate to achieve the desired goal of minimizing the loss A Parameter a/the Adaptive Firefly Algorithm The population size N for IEEE 14 bus system and IEEE 30bus system are taken as 30 The maximum number of Iterations considered as 200 The random movement factor a are tuned during each iteration The initial value of a is set to 05 The attractiveness parameter fj is varied from fjmin to "ax The value of fjmin is taken as 02 and the value of "ax is taken as 1 The absorption parameter r is taken as 1 and it is tuned in all iteration It has to be pointed out that the performance of the entire metaheuristic optimization algorithm is very dependent on the tuning of their different parameters A small change in the parameter may result in a large change in the solution of these algorithms Self adaptive Firefly Algorithm is a powerful algorithm which efficiently tuned all the parameters to obtain the global or near global optimal solution VI CONCLUSION The optimal location of FACTS devices play a vital role in achieving the proper functioning of these devices However this paper made an attempt to identify the optimal location and parameter of TCSC which minimizes the transmission loss in the power system network using Self Adaptive Firefly algorithm Simulations results are presented for IEEE14bus and IEEE30 bus systems Results have shown that the identified location of TCSC minimize the transmission loss in the power system network With the above proposed algorithm it is possible for utility to place TCSC devices in transmission network such that proper planning and operation can be achieved with minimum system losses REFERENCES [1] N G Hingorani and LGyugyi,Understanding FACTS Concepts and Technology of Flexible AC Transmission Systems, New York:IEEE Press,2000 [2] RMMathur and RKVarma, Thyristorbased FACTS Controllers for Electrical Transmission Systems, Piscataway: IEEE Press,2002 [3] DrHAmbrizperez,EAchaand,CRFuerteEsquivel "Advanced SVC models for newton raphson load flow and newton optimal power flow studies,"ieee Systems, vol 15,pp ,2000 Transaction on Power [4] TOrfanogianni, "A Flexible software environment for steady state power f10w optimization with series FACTS devices,"diss ETH Zurich 2000,13689 [5] E YLarsen,K Clark, SAMiske,Jr,andJ Urbanek, "Characteristic and rating considerations of thyristor controlled series compensation," IEEE Transaction Power Delivery,voI9,pp ,1994 [6] DrLGyugyi, "Unified power flow controler concept for flexible AC transmission system,"in lee proceedings, vol I 39,no4pp I, July, 1992 [7] X S Yang,NatureInspired MetaHeuristic Algorithms, 2nd ed, Beckington, Luniver Press,20 I O [8] X SYang, Firefly algorithms for multimodal optimization, Stochastic algorithms: Foundations and applications, SAGA 2009, LNCS, Berlin, Germany: SpringerVerlag, 2009, vol 5792, pp [9] SGerbex, RCherkaom, and AJGermond, "Optimal location of multi type FACTS devices in a power systems by means of genetic algorithms," IEEE Transaction on Power SystemsvoI16, no 3, pp , 2001 [10] M Saravanan, S M R Slochanal, P Venkatesh, J P S Abraham, "Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability," Electrical Power System Research, vol77, pp , 2007 [11] Hirotaka Yoshida, Kenichi Kawata, Yoshikazu Fukuyama,"A Particle swarm optimization for reactive power and voltage control considering voltage security assessment," IEEE Transaction on Power Systems, vo1l5,no4, ppl , Nov 2001, [12] Sidhartha Panda, Narayana Prasad Padhy, "Optimal Location and controller design of ST ATCOM for power system stability improvement using PSO," Journal of the Franklin Institute, voi345, pp , Mar 2008, [13] D Mondal, A Chakrabarti, A Sengupta, "Optimal placement and parameter setting of SVC and TCSC using PSO to mitigate small signal stability problem," International Journal of Electrical Power & Energy Systems, voi42,nol,pp , Nov 2012 [14] M Senthil Kumar, P Renuga, "Application of UPFC for enhancement of voltage profile and minimization of losses using fast voltage stability index(fvsi), "Archives Of Electrical Engineering Journal, vol61, no2, pp , Jun 2012 [15] X S Yang,"Firefly algorithm stochastic test functions and design optimization," International Journal of Bioinspired Computation, vol 2,pp 7884,2010 [16] T Apostolopoulos, and A Vlachos, "Application of the Firefly algorithm for solving the economic emissions load dispatch problem," International Journal of Combinatorics, vol2011, no523806, p23, 2011 [17] XS Yang, S S Hosseini, and AH Gandomi, "Firefly algorithm for solving nonconvex economic dispatch problems with valve loading eflect," Applied Soft Computing, vol 12, Issue 3, pp , Mar 2012 [18] R Falcon, M Almeida, and A Nayak, "Fault identification with binary adaptive firef1ies in parallel and distributed systems," in proc IEEE Congress on Evolutionary Computation, Jun 2011pp [19] S Palit, S Sinha, M Molla, A Khanra, and M Kule, "A Cryptanalytic attack on the knapsack cryptosystem using binary firefly algorithm," in proc2nd international Conference on Computer and Communication Technology (ICCC!), Sep 2011, pp [20] K Chandrasekaran and S P Simon, "Demand response scheduling in SCUC problem for solar integrated thermal system using firefly algorithm," in proc iet Conference on Renewable Power Generation (RPG 2011), 2011, pp 18 [21] K Chandrasekaran and S P Simon, "Network and reliability constrained unit commitment problem using binary real coded 687
6 I Proceedings of the "International Conference on Advanced Nanomaterials & Emerging Engineering Technologies" (ICANMEET20J3) organized by Sathyabama University, Chennai, India in association with DRDO, New Delhi, India, 24 t h _26tl" July, 2013 LiiII :J_ firefly algorithm," International Journal of Electrical Power & Energy Systems, vo143, Issuel, pp , Dec2012 [22] MH Homg and R J Liou, "Multilevel minimum cross entropy threshold selection based on the firefly algorithm," Expert Systems with Applications, vol 38, Issue 12, pp , 20 II [23] EAcha,CRFuerteEsquivel, Hugo Ambrizperz, CAugeless Cameto, FACTS Modelling and simulation in power network, 1 sl ed New York: John Wiley & Sons Ltd, 2004 [24] Power Systems Test Case, The University of Washington Archive, [Online ]Available: ee washingtonedu/research/pstca/, [25] Taher Nlknam, Rasoul AzizipanahAbarghooee, and Alireza Roosta, "Reserve Constrained Dynamic Economic Dispatch:A New Fast SelfAdaptive Modified Firefly Algorithm," IEEE System Journal, vol6, no 4, Dec
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