International Journal of Recent Research and Review, Vol. III, September 2012 ISSN

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1 International Journal of Reent Researh and Review, Vol. III, September 2012 ISSN Redution in Power Losses in Distribution Lines using Bioni Random Searh Plant Growth Simulation Algorithm Tanuj Manglani 1, Y.S.Shishodia 2 Assistant Professor, Department of Eletrial Engineering, YIT, Jaipur, India Pro. VC, JaganNath University, Jaipur, India idtanuj@g mail.o m Abstrat - To redue the losses in distribution systems, bioni random searh plant growth simulation algorithm (PGSA) has been proposed using optimal apaitor plaement. The main advantage of the proposed method is that it does not require any external ontrol parameters. The proposed method is implemented on IEEE 34 bus radial distribution system. The solutions obtained by PGS A give better results than fuzzy logi method. Index Terms - Capaitor plaement, apaitor sizing, optimal apaitor alloation, distribution system, power loss redution, PGS A, Random searh algorithm, Bioni searh. I. INTRODUCTION In the present senario of energy risis, saving of the power has beome a major issue. Studies have indiated that 13% of total power is wasted in the form of losses at the distribution level [1]. The redution of losses in the power networks is muh more benefiial than the inrease of generating apaities. Losses an be redued by onneting apaitors in shunt to loally supply a onsiderable portion of the reative power demanded by the onsumers and thereby reduing the reative omponent of branh urrents [2]. Therefore, it is important to find optimal loation and sizes of apaitors in the system to redue power losses. Various optimization tehniques and algorithms have been proposed in the past to redue the power losses suh as algorithm based on analytial methods [3-4], numerial programming methods [5-6], heuristis methods [7-8], geneti algorithm [9-11], simulated annealing [12-13],fuzzy logi [14-15],PSO[16-17] and ant olony algorithm [18]. In this paper, power losses will be redued by using apaitor plaement with the help of Plant Growth Simulation Algorithm (PGSA). The proposed method is tested on 34 bus radial distribution systems. II. PROBLEM FORMULATION The objetive of the apaitor plaement in the distribution system is to minimize the total annual power losses in suh a way that the total annual ost of the system gets redued under ertain operating onstraints. Newton Raphson method is used to alulate the total power loss of the system under study. The three-phase system is onsidered as balaned and the loads are assumed as time invariant. The ost of the operation and maintenane of the apaitor plaed in the system are assumed to be negleted. The largest apaitor size an not be more than total reative load at a partiular bus [19].Mathematially, the objetive funtion [20] of the problem is the ost whih inludes the ost equivalent to redution of power loss in the system and the ost of apaitors plaement whih is to be minimized under ertain onstraints as; Minimize Objetive funtion: COST KPP n + K Q T, Loss i i (1) i 1 Subjet to Voltage limit: V min,i V i V max,i (2) Capaitor size limit: n  Q i Q i 1 Li (3)

2 Here, n is the number of buses, Kp is the equivalent annual ost per unit of power loss in $/kw/year, Ki the annual apaitor installation ost $/kvar, V i is the voltage magnitude of bus i, V min,i and V max,i are minimum and maximum voltage limits of i th bus respetively, bus i and and III. Qi is the reative power ompensated at QLi is the reative load power at bus i. IDENTIFICATION OF OPTIMAL LOCATION Optimal loations for apaitor plaements are the seleted buses that an be determined using Loss Sensitivity Fators. The estimation of these buses helps in redution of the searh spae for the optimization proedure. A distribution line with an impedane R+jX and a load of P eff + jq eff onneted between p and q buses is given below Fig. 1. p R+jX k th Line q P eff+jqeff Fig. 1 Distribution Line with p and q Buses Ative power loss in the K th line is given by [ I ] 2 k * R[ k] P lineloss [ q] ( P whih an be expressed as, 2 eff [ q] + Q 2 eff [ q] R[ k]) ( V[ q])*( V[ q]) (4) Where, Peff [q] & Qeff [q] are the total effetive ative and reative power respetively supplied beyond the node q. Now, the Loss Sensitivity Fators [21] an be given by Plineloss (2* Qeff[ q]* R[ k]) (5) Qeff ( V[ q]) *( V[ q]) Loss Sensitivity Fators ( Plineloss / Qeff ) are alulated from load flow analysis of the given system and the values are arranged in desending order for all the lines of the system. The desending order will deide the sequene in whih the buses are to be onsidered for ompensation [22]. If the nominal voltage (i.e. V[i] / 0.95) at a bus in the sequene list is greater than 1.01then suh bus needs no ompensation. is IV. PLANT GROWTH SIMULATION ALGORITHM Plant Growth Simulation Algorithm (PGSA) is a new type of intelligent optimization algorithm whih is based on a omputer system (namely L-systems) proposed by A. Lindenmayer and P.Prusinkiewiz in the 1990s. L-system is used in the fratal domain and the omputer graphis to simulate the plant growing and branhing proess. When plant outgrows, the main drive to promote its growth impetus omes from the sunlight, the auxin onentrations in plant are hanged with photosynthesis, and the point whih has reeived suffiient sunlight with more onentrations will grow prior. By using plant growth simulation algorithm to solve optimization problems is atually a simulation proess of plant outgrowing to the whole spae. The point whih an outgrow a new branh in the plant is alled growing point, the more auxin onentration of the growing point, the more growing opportunities it gets. The auxin onentration of plant is mainly deided by phototropi, it will be reassigned among eah growing points if the environment loation is hanged. Tong Li et al. [23] analyzed the probability growth model of simulating the plants phototropi, and gives out auxin onentration alulating formulas of the stems and branhes. Assuming a plant grows a trunk M from its root and there are k initial growing points alled nodes N M1, N M2, N M3.. N Mk that have better environment than the root N 0 on the trunk M. The auxin onentration S M1,S M2..S Mk of the nodes N M1, N M2.. N Mk an be alulated by S Mi k f ( N 0) - f ( NMi) (6) [ f ( N 0) f ( NMi) ] Â - i 1 The auxin onentration of the growing point an be more if the funtion of the nodes N M1, N M2, N M3.. N Mk and N 0 satisfy f(n Mi ) < f(n o ) for i=1,2,3.k. From (6), it an be alulated that summation of all onentration is equal to unity, whih means that the auxin onentrations S M1,S M2..S Mk of the orresponding nodes N M1, N M2.. N Mk form a state spae shown in Fig 2.

3 Fig. 2 Auxin Conentration State Spae Now randomly generates a number within [0, 1] and drop into one of S M1,S M2..S Mk as shown in Fig.2, the node orresponding to the seleted onentration will be the next growing point. Repeat the above proess until there is no new branh to grow and hene a omplete plant will be formed. Chung Wang et al. [24] suggested a model where the nodes on a plant an express the possible solutions, f(n) an express the objetive funtion, the length of the trunk and the branh an express the searh domain of possible solutions, the root of a plant an express the initial solution, the preferential growth node orresponds to the basi point of the next searhing proess. In this way, the growth proess of plant phototropism an be applied to solve the problem of integer programming. V. APPLICATION OF PROPOSED METHOD The proposed method has been programmed using MATLAB. The effetiveness of the proposed method for loss redution by apaitor plaement is tested on 34-bus test radial distribution systems [25]. This system has a main feeder and four laterals (subfeeders). The single line diagram is shown in Fig. 3. The line and load data of the feeders are shown in Table I. The rated line voltage of the system is 11 kv. S Fig.3 34-Bus Distribution Network of sensitive analysis is used to selet the andidate installation loations of the apaitors to redue the searh spae. The buses are ordered aording to their sensitivity value ( Plineloss / Qeff ) (i.e., bus 19, 22, 20, 21, 23, 24, 25, 26, and 27). Now, the apaitors are plaed on these seleted buses using PGSA with all possible ombinations of buses. Fig. 4 shows the graph between number of buses used for apaitor plaement and the orresponding losses in kw. The optimum loations with size of apaitors with the proposed method are ompared with the Fuzzy reasoning [27] and are shown in Table III. The minimum voltage before apaitor plaement is p.u at bus number 27 whih has improved to p.u after apaitors plaement Table III Simulation Results of 34- Bus System and Its Comparison Parameters Total losses (kw) Loss redution (%) Optimal loation and size in kvar Annual Cost ($ / year) Net Saving ($ / year) Unompensated Fuzzy Reasoning [27] Proposed PGSA , ,778 8,172 8,404 From the results shown in Table III, it is observed that the power loss obtained with proposed method is less than Fuzzy Reasoning method. Also, the annual ost after the apaitor plaement by PGSA method is less than that of Fuzzy Reasoning method. For this test feeder, K P is seleted is seleted to be 168$/(kW-year) [19]. Only fixed apaitors with a life expetany of 10 years (plaement and maintenane osts are negleted) are used in the analysis and the i marginal ost of apaitors ( K ) [26] given in Table II are used to ompute the total annual ost. The method

4 Fig.4 Losses Corresponds to Number of Candidate Buses VI. CONCLUSION A bioni random searh Plant Growth Simulation Algorithm (PGSA) for loss redution in the distribution system has been proposed. The loss sensitivity fators are used to determine the andidate loations of the buses required for ompensation. The PGSA is used to estimate the required level of shunt apaitive ompensation at the optimal andidate loations to redue the ative power loss. The test results of 34 bus system have showed the best solutions than other approah available in the referene. PGSA have many harateristis suh as fewer parameters, easily oding and implement, fast alulating speed, no more restritions or requirements in solving the objetive funtion. In this paper, apaitors at less number of loations with optimum size are plaed whih results in net annual saving. Also, pratial values of apaitors are onsidered with a finite number of sizes rather than ontinuous apaitors size. VII. REFERENCES [1] Y. H. Song, G. S. Wang, A. T. Johns and P.Y. Wang, Distribution network reonfiguration for loss redution using Fuzzy ontrolled evolutionary programming, IEEE Trans. Gener., Trans., Distri., Vo l.144, No..4, July [2] H. D. Chiang, J. C. Wang, O. Cokings, and H. D. Shin, Optimal apaitor plaements in distributions systems: Part 1 and 2, IEEE Trans. Power Del., Vol. 5, No. 2, pp , Apr [3] M. E. Baran and F. F. Wu, Optimal Capaitor Plaement on Radial Distribution Systems, IEEE Trans. on Power Delivery, Vol. 4, No. 1, January [4] M. M. A. Salama and A. Y. Chikhani, A simplified network approah to the VAR ontrol problem for radial distribution systems, IEEE Trans. Power Delivery, vol. 8, no. 3, pp , Jul [5] S. Civanlar and J. J. Grainger, " Var Control on Distribution Systems with Lateral Branhes Using Shunt Capaitors and Voltage Regulators: Part 1. Part 11, Part III", IEEE Trans. on Power Apparatus and systems, vol. 104, pp , Nov [6] J. J. Grainger, and S. H. Lee, "Optimum Size and Loation of Shunt Capaitors for Redution of Losses on Distribution Feeders", IEEE Trans. on Power Apparatus and Systems, vol. 100, pp , Marh [7] M. Chis, M. M. A. Salama, and S. Jayaram, Capaitor plaement in distribution systems using heuristi searh strategies, Pro. Inst. Elet.Eng. Gen. Transm. Dist., vol. 144, no. 3, pp , [8] M. M. Hamada, M. A. A. Wahab, A. M. El-Sayed, H. A. Ramadan, A proposed strategy for apaitor alloation in radial distribution feeders, in Conf. 12th Middle East Power Systems Conf., MEPCON, Aswan, Egypt, Marh 2008 [9] S.Sundhararajan and A. Pahwa, Optimal seletion of apaitors for radial distribution systems using a geneti algorithm, IEEE Trans. Power Systems, vol. 9, no.3, pp , Aug [10] Dong Zhang Zhengai Fu and Liuhun Zhang, Joint Optimization for Power Loss Redution in Distribution Systems, IEEE Trans. Power Syst., vol.23, no.1, Feb. 2008, pp [11] S.Jalilzadeh, S. Galvani, H. Hosseinian, F.Razavi, Voltage Profile Modifiation Using Geneti Algorithm In Distribution Systems, in Proeedings of the World Congress on Engineering and Computer Siene 2007 WCECS 2007, Otober 24-26, 2007 [12] T. Ananthapadmanabha, A. D. Kulkarni, A. S. Gopala Rao, and K. Raghavendra Rao, Knowledge-based expert system for optimal reative power ontrol in distribution system, Eletrial Power & Energy Systems, vol. 18, no. 1, pp , 1996 [13] Mekhamer, S.F., Khattab, H.M., Mahmoud, A.M.A., Solution of the Capaitor alloation problem in distribution feeders onsidering load variation: A modified simulated annealing based approah in Pro. Int. Conf. power Systems Conferene, (MEPCON 2006), De. 2006, pp [14] S. M. Kannan, A. Rathina Grae Monia, and S. Mary Raja Slohanal, Fuzzy Logi Based Optimal Capaitor Plaement on Radial Distribution Feeders, in Pro. Int. Conf. Power System Tehnology and IEEE Power India Conferene, 2008(POWERC ON 2008), Ot. 2008,pp [15] H Masoum, M.A.S., Jafarian, A., Ladjevard i, M., Fuhs, E.F., Grady, W.M., Fuzzy approah for

5 optimal plaement and sizing of apaitor banks in the presene of harmonis, IEEE Trans. Power on Delivery, vol. 19, no.2, pp , Apr [16] AlRashidi, M.R., El-Hawary, M.E., A Survey of Partile Swarm Optimization Appliations in Eletri Power Systems, IEEE Transations on Evolutionary Computation, vol. 13, no.4, pp , Aug [17] Etemadi, A.H., Fotuhi-Firuzabad, Distribution system reliability enhanement using optimal apaitor plaement, Generation, Transmission & Distribution, IET, vol. 2, no. 5, pp , July [18] Chung-Fu Chang, Reonfiguration and Capaitor Plaement for Loss Redution of Distribution Systems by Ant Colony Searh Algorithm, IEEE Trans. Power Syst., vol. 23, no. 4, pp ,Nov [19] Y. Baghzouz and S. Ertem, Shunt Capaitor Sizing for Radial Distribution Feeders with Distorted Substation Voltages, IEEE Trans Power Delivery, Vol. 5, No. 2, pp , April [20] J. J. Grainger and S. H. Lee, Capaity Release by shunt apaitor Plaement on distribution Feeders, IEEE Trans. on Power Apparatus and Systems, Vol. 101, No. 5, pp , Marh [21] Prakash K. and Sydulu M, Partile swarm optimization based apaitor plaement on radial distribution systems, IEEE Power Engineering Soiety general meeting, pp. 1-5, [22] Su C. T and Tsai C. C, A new fuzzy reasoning approah to optimum apaitor alloation for primary distribution systems, Pro IEEE on Industrial Tehnology Conf, 1996; pp [23] Tong Li, Chunfeng Wang and Wenbo Wang et al., A global optimization bionis algorithm for solving integer programming- plant growth simulation algorithm, Systems Engineering-Theory and Pratie, vol.25, no.1, pp.76-85,2005 [24] Chun Wang and Hao Zhong Cheng, Optimization of network reonfiguration in large distribution systems using plant growth simulation algorithm, IEEE Trans. on Power Systems, Vol.23, No.1, pp , Feb [25] M. Chis, M. M. A. Salama, and S. Jayaram, Capaitor plaement in distribution systems using heuristi searh strategies, Pro. Inst. Elet. Eng., Vol. 144, No. 2, pp , May [26] S. F. Mekhamer, New heuristi strategies for reative power ompensation of radial distribution feeders, IEEE Trans Power Delivery, Vol. 17, No. 4, pp , Otober [27] H. N. Ng, M. M. A. Salama and A. Y. Chikhani, Capaitor Alloation by Approximate Reasoning: Fuzzy Capaitor Plaement, IEEE Trans. Power Delivery, Vol. 15, No.1, pp , Jan

6 Bus No. Table I Load Data and Feeder Data of 34-Bus System Load Setional Parameters Length P Q Bus No R i,i+1 X i,i+1 km (kw) (kvar) From To (ohm/km) (ohm/km)

7 Table II Possible Size of Capaitors in $/kvar Q j $/kvar J Q j $/kvar J Q j