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A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
A new simplified approach for optimum allocation of a distributed generation
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A new simplified approach for optimum allocation of a distributed generation

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  • 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME & TECHNOLOGY (IJEET)ISSN 0976 – 6545(Print)ISSN 0976 – 6553(Online)Volume 4, Issue 2, March – April (2013), pp. 165-178 IJEET© IAEME: www.iaeme.com/ijeet.aspJournal Impact Factor (2013): 5.5028 (Calculated by GISI) ©IAEMEwww.jifactor.com A NEW SIMPLIFIED APPROACH FOR OPTIMUM ALLOCATION OF A DISTRIBUTED GENERATION UNIT IN THE DISTRIBUTION NETWORK FOR VOLTAGE IMPROVEMENT AND LOSS MINIMIZATION Dr.T.Ananthapadmanabha1, Maruthi Prasanna.H.A. 2, Veeresha.A.G. 2, Likith Kumar. M. V 2 1 Professor, Dept of EEE, NIE, Mysore, Karnataka, India. 2 Research Scholar, Dept of EEE, NIE, Mysore, Karnataka, India. ABSTRACT In the present energy scenario, increased concerns are shown towards distributed generation (DG) driven by renewable energy resources. DG is a small scale generation units that are connected near to customer load center or directly to the distribution network. Such DGs has the capability of altering power flows, system voltages, and the performance of the integrated network. When DGs are integrated to existing distribution network, offers many techno-economical benefits. To maximise the availing benefits, optimal DG planning is necessary. The two critical issues of DG planning are : Optimal Placement of DG & Optimal sizing of DG. The problem of optimal allocation of DG in the existing distribution system plays an important role in planning and operation of Smart Electrical Distribution Systems, which is the state of the art development in power system. In this paper, the optimal location of a DG is found out by using a new index called ‘TENVDI’ & the optimal sizing of DG at the optimal location is decided for loss minimisation. The proposed methodology has been tested on standard IEEE-33bus radial distribution system & IEEE-69bus radial distribution system using MATLAB 2008. The method has a potential to be a tool for identifying the best location and rating of DG to be installed for improving voltage profile and reducing line losses in a distribution system. KEYWORDS: RDS (Radial Distribution System), DG (Distributed Generation), TEN (Tail End Node), VDI (Voltage Deviation Index). 165
  • 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME1. INTRODUCTION Due to limitation on fossil fuel resources, alternative solutions to traditional largepower stations areunder high priority in recent years to meet growing energy demand of thefuture [1]. Distributed Generation (DG) usually refers to the power generation from a fewkilowatts to hundreds of megawatts ( and some proposed restrictions under 50MWs) of thesmall scale, distributed, efficient, reliable power generation unit which is arranged around theuser [2].The IEEE defines DG is the generation of electricity by facilities that are sufficientlysmaller than central generating plants so as to allow interconnection at nearly any point in apower system [2].DG is an approach that employs small scale technologies to produceelectricity close to the end users of power. DG technologies often consist of modular (andsometimes renewable energy) generators, and they offer a number of potential benefits. Inmany cases, DGs can provide lower cost electricity and higher power reliability and securitywith fewer environmental consequences than can traditional power generators.DGtechnologies include small gas turbines, wind turbines, small combined cycle gas turbines,micro turbines, solar photovoltaic, fuel cells, biomass and small geothermal generatingplants. Determining the suitable location and sizing of a DG is important in order to ensurefor maximum benefits to be obtained from the integration of DG with the distribution system.with proper planning of DG integration the following technical and economical benefits suchas Voltage support and power quality improvement, Utility system reliability improvement,Voltage profile improvement, Spinning reserve support during generation outages, Reductionin line losses and hence reduce demand for the grid, Environmental impact in terms ofreduction in polluting emission as compared with traditional power plants, Transmission anddistribution costs can be reduced since the DG units are closer to the customers, DG isavailable in small modular units and therefore easier to find for their resulting in sites shortlead times for procurement and installation, DG plants offer good efficiencies especially inco-generations and combined-cycles (for larger plants) and many more. The mainapplications of DG can be found in the applications involving Base load, Standby Power,Stand alone systems, Peak load shaving, Rural and remote applications, Combined Heat &Power (CHP), & Grid support. In literature, there are a number of approaches developed for placement and sizing ofDG units in distribution system. Chiradeja and Ramkumar [3] presented a general approachand set of indices to assess and quantify the technical benefits of DG in terms of voltageprofile improvement, line loss reduction and environmental impact reduction. Khan andChoudhry [4] developed an algorithm based on analytical approach to improve the voltageprofile and to reduce the power loss under randomly distributed load conditions with lowpower factor for single DG as well as multi DG systems. Hung et al. [5] used an improvedanalytical method for identification of the best location and optimal power factor for placingmultiple DGs to achieve loss reduction in large-scale primary distribution networks. Foroptimal placement of DG, Mithulanathan et al. [6] presented a genetic algorithm basedapproach to minimize the real power loss in the system and found a significant reduction inthe system loss. The optimal sizing and siting of DGs was investigated by Ghosh et al. [7] tominimize both cost and loss with proper weighing factors using Newton-Raphson (NR) loadflow method. Ziari et al. [8] proposed a discrete particle swarm optimization and geneticalgorithm (GA) based approach for optimal planning of DG in distribution network tominimize loss and improve reliability. Kamel and Karmanshahi [9] proposed an algorithm for 166
  • 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEMEoptimal sizing and siting of DGs at any bus in the distribution system to minimize losses andfound that the total losses in the distribution network would reduce by nearly 85%, if DGswere located at the optimal locations with optimal sizes. Singh et al. [10] discussed a multi-objective performance indexbased technique using GA for optimal location and sizing of DGresources in distribution systems. This paper presents a simple method for voltage profile improvement, real power lossreduction, substation capacity release and is based on tail end nodes voltage sensitivityanalysis. Power flow analysis is done using the forward-backward sweep method. Test resultscarried out on IEEE-33 bus system & IEEE-69 bus system using MATLAB 2008 validatesthe suitability of this proposed method.2. NOMENCLATURENn : Total number of nodes or buses in the given radial distribution system.TENVDI : Tail End Nodes Voltage Deviation Index (matrix of order Nn X 1)TENVDIi : Tail End Nodes Voltage Deviation Index evaluated by placing DG at bus number i.NTE : Number of Tail End Nodes.SDG : Complex Power rating of DG in MVASDGmin & SDGmax : Minimum & Maximum Complex Power rating of DG in MVAPloss, Qloss, & Sloss : Real Power, Reactive Power & Complex Power loss in distribution systemSDGopt : Optimal Size of DG (Complex power rating in MVA)SDopt : Complex demand at optimal location in MVA SDG : Incremental value of Size of DG (Complex power rating in MVA)3. PROPOSED METHODOLOGY The optimal allocation of DG problem consists of three important steps. Viz Selectionof Load flow analysis technique, finding optimal location and selection of optimal size of DG.3.1 LOAD FLOW ANALYSIS Conventional NR and Gauss Seidel (GS) methods may become inefficient in theanalysis of distribution systems, due to the special features of distribution networks, i.e. radialstructure, high R/X ratio and unbalanced loads, etc. These features make the distributionsystems power flow computation different and somewhat difficult to analyze as compared tothe transmission systems. Various methods are available to carry out the analysis of balancedand unbalanced radial distribution systems and can be divided into two categories. The firsttype of methods is utilized by proper modification of existing methods such as NR and GSmethods. On the other hand, the second group of methods is based on backward and forwardsweep processes using Kirchhoff’s laws. Due to its low memory requirements, computationalefficiency and robust convergence characteristic, backward and forward sweep basedalgorithms have gained the most popularity for distribution systems load flow analysis. In thisstudy, Backward and Forward sweep method [11] is used to find out the load flow solution.3.2 OPTIMAL PLACEMENT OF DG USING TENVDI : In order to restrict solution space to few buses, tail end nodes are first identified byviewing the distribution network topology. By penetrating DG with 50% of the total feeder 167
  • 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEMEloading capacity at each node at a time, the Tail End Nodes Voltage Deviation Index(TENVDI) is calculated using (1). When DG is connected at bus i, TENVDI for bus i isdefined as: ሺ௏௡௢௠௜௡௔௟ି௏௠ሻ మ TENVDIi = ∑ே்ா ௠ୀଵ ே்ா --- (1)Where, ‘m’ corresponds to the each tail end node element of Tail End Nodes (TEN) matrix oforder NTE X 1 ; Vnominal is taken as 1.0 Pu ;TENVDIi gives the total deviation of voltages of all tail end nodes of the network withrespect to the nominal voltage. The bus corresponding to the minimum TENVDI value whenDG is inserted at the same bus is the optimal location of DG in the distribution system. Theflowchart for finding optimal location for DG placement is shown in fig1. Figure 1: Flowchart for finding optimal location of DG in distribution system using TENVDI 168
  • 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME3.3 OPTIMAL SIZING OF DG AT OPTIMAL LOCATION: For deciding the optimal size of DG to be placed at the optimal location obtainedfrom TENVDI, the DG is inserted at the optimal bus, size is varied from minimum value(SDGmin) to maximum value (SDGmax) with step size of ( SDG). The size which gives theminimum complex power loss is the optimal size of DG to be placed at optimal location. Theflowchart for determining the optimal size of the DG to be placed at optimal location for lossminimisation is shown in fig2. Figure 2: Flowchart for determinign optimal size of DG at optimal location for loss minimisation 169
  • 6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME4. SIMULATION RESULTS AND DISCUSSION4.1 IEEE-33 BUS RADIAL DISTRIBUTION SYSTEM The distribution system characteristics: Number of buses=33; Number of lines=32;Slack Busno=1; Base Voltage=12.66KV; Base MVA=100 MVA; The test system is simulated in MATLAB2008 & the proposed methodology has been tested, whose results are as shown below. Figure 3: Single line diagram of standard IEEE-33 Bus system Table 1: Tail End Node matrix elements Sl.no Tail End Nodes 1 18 2 22 3 25 4 33 Table 2:Base case Bus Voltages for IEEE-33BUS test system Bus Bus Bus Bus Bus Bus no Voltage no Voltage no Voltage (Pu) (Pu) (Pu) 1 1.0000 12 0.9177 23 0.9793 2 0.9970 13 0.9115 24 0.9726 3 0.9829 14 0.9093 25 0.9693 4 0.9754 15 0.9078 26 0.9475 5 0.9679 16 0.9064 27 0.9450 6 0.9495 17 0.9044 28 0.9335 7 0.9459 18 0.9038 29 0.9253 8 0.9323 19 0.9965 30 0.9218 9 0.9260 20 0.9929 31 0.9176 10 0.9201 21 0.9922 32 0.9167 11 0.9192 22 0.9916 33 0.9164 170
  • 7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Figure 4: Basecase Voltage profile for IEEE-33bus system Table 3: Variation of TENVDI with DG Placement Bus TENVDI Bus TENVDI Bus TENVDI no (x10-4) no (x10-4) no (x10-4) 1 5.231 12 0.913 23 3.969 2 5.028 13 1.378 24 3.914 3 4.049 14 1.681 25 4.005 4 3.471 15 2.009 26 1.668 5 2.918 16 2.452 27 1.558 6 1.755 17 3.593 28 1.229 7 1.525 18 4.201 29 1.137 8 0.894 19 5.019 30 1.141 9 0.775 20 5.172 31 1.289 10 0.832 21 5.297 32 1.378 11 0.856 22 5.611 33 1.513Figure 5: Variation of TENVDI with DG Placement Figure 6:Variation of Tail End Node Voltage with DG Placement 171
  • 8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Table 4 : Comparison of Complex Power Losses for Optimal sizing of DG at Optimal location: Bus 9 Optimal Complex Power Loss (Sloss) in Location = Bus KVA 9 DG Rating in Case1 Case2 Case3 MVA (Unity Pf) (0.9Pf lag) (0.8Pf lag) 0.5 193.9777 182.1617 182.1227 1.0 159.0668 136.2883 136.1958 1.5 147.3413 113.8010 113.5875 2.0 156.6458 112.0736 111.6356 2.5 185.1807 128.9607 128.1659 3.0 231.3957 162.6299 161.3234 3.5 293.8651 211.3234 209.3202 4.0 371.4385 273.8590 270.9834 Minimum Loss 147.3413 112.0736 111.6356 Optimal DG 1.5 2.0 2.0 capacity (SDGopt) in MVA Figure 7: Comparison of complex power losses after placement of DG for different cases Figure 8: Comparison of System Voltage Profile after DG placement (3 cases) with base case 172
  • 9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Table 5: Improvement of system parameters with optimal allocation of DGParameters Base Case Case I Case II CaseIIIActive Power losses in Pu 0.211 0.1215 0.0908 0.0902Reactive Power losses in Pu 0.143 0.0834 0.0643 0.0644Active Power drawn from Substation in Pu 3.926 2.3365 2.0058 2.2052Reactive Power drawn from Substation in Pu 2.443 2.3834 1.4925 1.1644 As per the flowchart of fig.1, the optimal location for DG having rating of 50% of totalcomplex demand of distribution system found to be Bus No: 9 (corresponding to minimumTENVDI). At this optimal location the optimum size of DG for loss minimisation for variouscases is given in table4. From fig 8, it is evident the optimal allocation of DG results in improvedvoltage profile..4.2 IEEE-69 BUS RADIAL DISTRIBUTION SYSTEM: The distribution system characteristics: Number of buses=69; Number of lines=68;SlackBus no=1; Base Voltage=12.66KV; Base MVA=100 MVA; The test system is simulated inMATLAB 2008 & the proposed methodology has been tested, whose results are as shown below. Table 6: Tail End Node matrix elements Sl.no Tail End Nodes 1 27 2 35 3 46 4 50 5 52 6 65 7 67Figure 9: Single line diagram of standard IEEE-69 Bus system 8 69 Figure 10: Basecase Voltage profile for IEEE-69bus system 173
  • 10. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Table 7:Base case Bus Voltages for IEEE-69 BUS test system Bus Bus Bus Bus Bus Bus no Voltage no Voltage no Voltage (Pu) (Pu) (Pu) 1 1.0000 24 0.9565 47 0.9998 2 1.0000 25 0.9564 48 0.9985 3 0.9999 26 0.9563 49 0.9947 4 0.9998 27 0.9563 50 0.9942 5 0.9991 28 0.9999 51 0.9785 6 0.9901 29 0.9999 52 0.9737 7 0.9808 30 0.9998 53 0.9746 8 0.9786 31 0.9997 54 0.9714 9 0.9774 32 0.9997 55 0.9669 10 0.9724 33 0.9995 56 0.9626 11 0.9713 34 0.9992 57 0.9401 12 0.9681 35 0.9992 58 0.9290 13 0.9652 36 0.9999 59 0.9248 14 0.9623 37 0.9997 60 0.9197 15 0.9594 38 0.9995 61 0.9123 16 0.9589 39 0.9994 62 0.9120 17 0.9580 40 0.9994 63 0.9117 18 0.9580 41 0.9983 64 0.9098 19 0.9576 42 0.9980 65 0.9092 20 0.9573 43 0.9979 66 0.9091 21 0.9568 44 0.9979 67 0.9091 22 0.9568 45 0.9978 68 0.9088 23 0.9567 46 0.9978 69 0.9088 Table 8: Variation of TENVDI with DG Placement Bus TENVDI Bus TENVDI Bus TENVDI no (x10-3) no (x10-3) no (x10-3) 1 0.3982 24 0.3137 47 0.3973 2 0.3980 25 0.3343 48 0.3969 3 0.3978 26 0.3434 49 0.3974 4 0.3973 27 0.3486 50 0.3980 5 0.3918 28 0.3978 51 0.2580 6 0.3298 29 0.3978 52 0.1536 7 0.2716 30 0.3986 53 0.2305 8 0.2583 31 0.3988 54 0.2084 9 0.2517 32 0.4004 55 0.1796 10 0.2443 33 0.4072 56 0.1533 11 0.2433 34 0.4328 57 0.0537 12 0.2416 35 0.4663 58 0.0263 13 0.2450 36 0.3978 59 0.0194 14 0.2546 37 0.3977 60 0.0138 15 0.2702 38 0.3978 61 0.0113 16 0.2737 39 0.3979 62 0.0115 17 0.2809 40 0.3979 63 0.0123 18 0.2810 41 0.4028 64 0.0221 19 0.2879 42 0.4070 65 0.0530 20 0.2925 43 0.4076 66 0.2206 21 0.3007 44 0.4078 67 0.2203 22 0.3011 45 0.4096 68 0.2101 23 0.3049 46 0.4096 69 0.2100 174
  • 11. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Table 9 : Comparison of Complex Power Losses for Optimal sizing of DG at Optimal location: Bus 61 Optimal Complex Power Loss (Sloss) in Location KVA = Bus 61 DG Case1 Case2 Case3 Rating in (Unity (0.9Pf lag) (0.8Pf MVA Pf) lag) 0.5 180.2229 162.6008 161.3606 1.0 128.9543 95.4359 92.9176 1.5 102.0178 53.8736 50.1355 2.0 96.7717 34.8566 30.0237 2.5 111.0474 35.7901 29.9683 3.0 143.0446 54.7633 48.1141 3.5 191.2309 90.1621 82.9125 4.0 254.1131 140.5060 132.8839 Minimum 96.7717 34.8566 29.9683 Loss Optimal DG capacity 2.0 2.0 2.5 (SDGopt) in MVAFigure 11: Variation of TENVDI with DG placement Figure 12:Variation of Tail End Node Voltage with DG Placement 175
  • 12. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Figure 13: Comparison of complex power losses after placement of DG for different cases Table 10: Improvement of system parameters with optimal allocation of DGParameters Base Case Case I Case II CaseIIIActive Power losses in Pu 0.2365 0.0872 0.0300 0.0254Reactive Power losses in Pu 0.1065 0.0420 0.0174 0.0152Active Power drawn from Substation in Pu 4.1272 1.9779 2.1206 1.9161Reactive Power drawn from Substation in Pu 2.8001 2.7356 1.8393 1.2088 Figure 14: Comparison of System Voltage Profile after DG placement (3 cases) with base case As per the flowchart of fig.1, the optimal location for DG having rating of 50% oftotal complex demand of distribution system found to be Bus No: 61 (corresponding tominimum TENVDI). At this optimal location the optimum size of DG for loss minimisationfor various cases is given in table9. From fig 14, it is evident the optimal allocation of DGresults in improved voltage profile. 176
  • 13. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME5. CONCLUSION The determination of size and location of DG are two important factors for the planning andoperation of smart electrical distribution systems. This paper presents a simplified approach foroptimum allocation of DG in distribution system in which the optimal location of DG is determinedby TENVD index for improving the tail end node voltages and optimal sizing of DG is determined atthe optimal location for minimising the power losses. The proposed method has been tested on IEEE-33bus system & IEEE-69bus system using MATLAB 2008. The results of these two systems haveproved the impact of optimal allocation of DG in terms of better voltage profile especially forconsumers connected to tail end node and reduced power losses.REFERENCES[1] A. Ipakchi and F. Albuyeh. Grid of the future. IEEE Power and Energy Magazine. 2009, 7 (2): 52-62.[2] T. Ackermann, G. Andersson and L. Soder. Distributed generation: a definition, Electrical PowerSystem Research. 2001, 57 (3): 195-204.[3] P. Chiradeja and R. Ramkumar. An approach to quantify the technical benefits of distributedgeneration. IEEE Transaction on Energy Conversion. 2004, 19 (4): 764-773.[4] H. Khan and M.A. Choudhry. Implementation of distributed generation algorithm for performanceenhancement of distribution feeder under extreme load growth. International Journal of ElectricalPower and Energy Systems. 2010, 32 (9): 985-997.[5] D.Q. Hung, N. Mithulanathan and R.C. Bansal. Multiple distributed generators placement inprimary distribution networks for loss reduction. IEEE Transactions on Industrial Electronics. (InPress).[6] N. Mithulanathan, T. Oo and L. V. Phu. Distributed generator placement in power distributionsystem using Genetic Algorithm to reduce losses. Thammasat International Journal on Science andTechnology. 2004, 9 (3): 55-62.[7] S. Ghosh, S.P. Ghoshal and S. Ghosh. Optimal sizing and placement of DG in network system.International Journal of Electrical Power and Energy Systems. 2010, 32 (8): 849-856.[8] I. Ziari, G. Ledwich, A. Ghosh, D. Cornforth and M. Wishart. Optimal allocation and sizing ofDGs in distribution networks. Proc of IEEE Power and energy society general meeting. 2010:1-8.[9] R.M. Kamel and B. Karmanshahi. Optimal size and location of DGs for minimizing power lossesin a primary distribution network. Transaction on Computer Science and Electrical and ElectronicsEngineering. 2009, 16 (2):137-144.[10] D. Singh, D. Singh and K.S. Verma. Multi-objective optimization for DG planning with loadmodels. IEEE Transactions on Power Systems. 2009, 24 (1): 427-436.[11] M.H. Haque. Efficient load flow method for distribution systems with radial or meshconfiguration. IEE Proc. On Generation, Transmission and Distribution. 1996, 143 (1): 33-38.[12] J.V.B. Subramanyam and C. Radhakrisna. Distributed Generation placement and sizing inunbalanced radial distribution system. World Academy of Science, Engineering and Technology.2009, 52: 737-744.[13] N. Acharya, P. Mahat and N. Mithulananthan. An analytical approach for DG allocation inprimary distribution network. International Journal of Electrical Power and Energy Systems. 2006, 28(10): 669–678.[14] N. Upadhyay and A.K.Mishra. A method for suitable location and capacity of distributedgeneration units in adistribution system. Proc. of 20th Australian university power engineering conference (AUPEC).2010.[15] M.A. Kashem, V, Ganapathi, G.B. Josman and M.I. Buhari. A novel method for lossminimization in distribution networks. Proc. of International Conference Electric Utilization,Deregulation Restructure, Power Tech. 2000: 251-256. 177
  • 14. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME 3(Online)AUTHORS’ Dr. T. Ananthapadmanabha received the B.E. degree in r. Electrical Engineering in 1980, M.Tech degree in Power Systems (1st Rank) in 1984 and Ph.D. degree (Gold Medal) in 1997 from and University of Mysore, Mysore. He is presently working as Professor in Department of Electrical and Electronics Engineering and Controller of Examinations at The National Institute of Engineering, Mysore, Karnataka, India. His research interest includes Reactive Power Optimization, Voltage Stability, Distribution Automation and AI applications to Power Systems. Maruthi Prasanna. H. A. received the Diploma in Electrical & Electronics Engineering in 2004 from D.R.R.Government Polytechnic, Davanagere and B.E. degree in Electrical & Electronics D Engineering in 2011 from B.M.S.Evening College of Engineering Engineering, Bangalore. He is presently pursuing research work at Department of Electrical and Electronics Engineering The National Institute of Engineering, Engineering, Mysore, Karnataka, India. ering, His research interest includes Distribution System Optimisation, Power System Stability studies, A.I. applications to power system and Smart Grid. Veeresha. A. G. received the B.E. degree in Electrical & Electronics Engineering in 2003 from SJMIT, Chitraduraga. He is presently pursuing research work at Department of Electrical and Electronics Engineering, The National Institute of Engineering, Engineering Mysore, Karnataka, India. , His research interest includes Wind Energy, Distribution System Design, Distributed Generation. Likith Kumar. M. V. received the B.E. degree in Electrical & Electronics Engineering in 2011 from SKIT, Bangalore. He is presently pursuing research work at Department of Electrical and Electronics Engineering, The National Institute of Engineering, tronics Engineering Mysore, Karnataka, India. , His research interest includes Smart Grid, Communication System, Renewable Energy. 178

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