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40220140504003
40220140504003
40220140504003
40220140504003
40220140504003
40220140504003
40220140504003
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40220140504003

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  • 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME 20 LOSS MINIMIZATION AND VOLTAGE IMPROVEMENT IN TRANSMISSION SYSTEMS USING MULTI DISTRIBUTED GENERATION P. Sobha Rani1 , A. Lakshmi Devi2 , K. Dhananjayababu3 1 Assoc. Professor, Dept. E.E.E., N.B.K.R.I.S.T., Vidyanagar 2 Professor, Dept.E.E.E., S,V.U. College of engineering, Tirupati 3 Research scholar, Dept.E.E.E., S.V.U.College of engineering, Tirupathi ABSTRACT Recent changes in the electric utility infrastructure have created opportunities for much technological innovation like employment of distributed generation (DG). DG devices can be strategically placed in power systems for grid reinforcement, reducing power losses and on-peak operating costs. It is important to define the size and location of local generation unit that is to be placed in the given transmission network. Optimal sizing and placement of DG unit reduces the power losses. In this paper for optimal location fuzzy approach is used. Particle swarm optimization (PSO) is used for optimal sizing of DG unit. The proposed method is tested on IEEE-14 bus system. Keywords: Distributed Generation (DG), Fuzzy Logic, Particle Swarm Optimization (PSO), Power Loss, Voltage. I. INTRODUCTION In the transmission line resistance, shunt conductance, inductance and shunt capacitance are distributed along the entire length of line. The overhead transmission lines are classified as: short, medium and long lines. There may be buses with only generators and no loads and others with only loads and no generators. Reactive power generators may also be connected to some buses. Distributed generation is defined as small scale generation, located at or near the load centers. DG devices can be strategically placed in power systems for grid reinforcement, reducing power losses, and on-peak operating costs, improving voltage profiles and improving system integrity, reliability and efficiency. Distributed generation has both positive and negative impact on power quality. This will depend on the type and amount of DG, as well as power interfaces and control schemes used to connect these units to grid. The development of DGs will bring new changes to traditional power systems. The beneficial effects of DG mainly depend on its location and size. Selection of optimal INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET) ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME: www.iaeme.com/ijeet.asp Journal Impact Factor (2014): 6.8310 (Calculated by GISI) www.jifactor.com IJEET © I A E M E
  • 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. location and size of DG is a necessary process to maintain the stability and reliabili system effectively before it is connected to power grid. II. PROBLEM FORMULATION In a large transmission system network with high power losses particular bus from many buses so as to place a DG unit for loss reduction. Power losses are present at every bus and identification of bus with highest power loss is important because losses at that bus includes majority of total losses in the sy minimizing power losses. This can be achieved by DG unit placement in the network. If DG size exceeds certain value of limit, power loss at that bus becomes negative. This situation must be avoided. In this paper the main focus is on voltage improvement and reduction of power loss for a transmission system using DG units. In this paper Newton Raphson method is used for load flow study. fuzzy logic approach for optimal placement of DG and Particle swarm optimization method for optimal size of DG. FUZZY LOGIC IMPLEMENTATION In this paper for location of DG on load buses developed with two objectives: reducing real power losses and maintaining voltage profile within the allowable limits (0.9p.u-1.1p.u). For writing fuzzy rules two inputs nodal voltages (p.u) are taken. LRi = Pi 1 –Pi 2 for i Where LR - loss reduction Pi 1 - real power for normal load flow Pi 2 - real power for load flow by total compensation of The loss reduction input is normalized using equation(2) so that values fall between 0 and 1, where the largest number having a value of 1 and PLI= The output of fuzzy gives the suitability index for DG placement. Maximum values are the promising locations for DG placement. Fig 1: Member International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME 21 location and size of DG is a necessary process to maintain the stability and reliabili before it is connected to power grid. a large transmission system network with high power losses, it is difficult to particular bus from many buses so as to place a DG unit for loss reduction. Power losses are present at every bus and identification of bus with highest power loss is important because losses at that bus includes majority of total losses in the system. The cost of power transmission is reduced by minimizing power losses. This can be achieved by DG unit placement in the network. If DG size exceeds certain value of limit, power loss at that bus becomes negative. This situation must be is paper the main focus is on voltage improvement and reduction of power loss for a transmission system using DG units. Raphson method is used for load flow study. This paper presents a ptimal placement of DG and Particle swarm optimization method for IMPLEMENTATION on of DG on load buses, fuzzy approach is used. Fuzzy logic is developed with two objectives: reducing real power losses and maintaining voltage profile within the 1.1p.u). For writing fuzzy rules two inputs- power loss for i= 1 to number of load buses ……… loss reduction real power for normal load flow real power for load flow by total compensation of DG at ith node The loss reduction input is normalized using equation(2) so that values fall between 0 and 1, where the largest number having a value of 1 and the smallest as 0. for i= 1 to no. of load buses ……. (2) The output of fuzzy gives the suitability index for DG placement. Maximum values are the promising locations for DG placement. Fig 1: Membership function used for power loss index International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), location and size of DG is a necessary process to maintain the stability and reliability of existing , it is difficult to select a particular bus from many buses so as to place a DG unit for loss reduction. Power losses are present at every bus and identification of bus with highest power loss is important because losses at that bus stem. The cost of power transmission is reduced by minimizing power losses. This can be achieved by DG unit placement in the network. If DG size exceeds certain value of limit, power loss at that bus becomes negative. This situation must be is paper the main focus is on voltage improvement and reduction of power loss for a This paper presents a ptimal placement of DG and Particle swarm optimization method for approach is used. Fuzzy logic is developed with two objectives: reducing real power losses and maintaining voltage profile within the power loss index (PLI) and ………(1) node The loss reduction input is normalized using equation(2) so that values fall between 0 and 1, ……. (2) The output of fuzzy gives the suitability index for DG placement. Maximum values are the
  • 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME 22 Fig 2: Membership function for node voltages Fig 3: Membership function used for fuzzy output Particle swarm optimization for optimal sizing Particle swarm optimization is a computational method that optimizes a nonlinear and multi- dimensional problem. Population of birds or fish is known as swarm. Each candidate of swarm is known as particle. These particles move around in the search space searching for their goal, the place which best suits their needs given by a fitness function. Once a problem space is defined a set of particles spawned in it and their positions and velocities are updated iteratively according to specific PSO algorithm. Every particle using eq (3) and (4) modifies its position to reach the global best position. Vi k+1 =kc [W Vi k +C1 randi (pbesti –Xi) + C2 randi (gbesti – Xi)] …… (3) Xi k+1 = Xi k + Vi k+1 .….. (4) Where kc =constriction factor Vi k =velocity of a particle I in kth iteration W = inertia weight parameter C1 , C2 = weight factors rand1 , rand2 =random numbers between 0 and 1 Xi k - position of particle in kth iteration Inertia weight is calculated using eq (5) for better exploration of search space.
  • 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME 23 W= Wmax– ሺௐ௠௔௫ିௐ௠௜௡ሻ‫כ‬௧ ் ….. (5) where Wmax , Wmin are the constraints for inertia weight factor t = current iteration count T = maximum number of iterations The constraints are Vi min ≤ Vi ≤ Vi max ……. (6) Xi min ≤ Xi ≤ Xi max …….. (7) Algorithm 1. Initialize (p*n) number of particles, where p is population size and n is number of DG devices. 2. (p*n) number of initial velocities are generated randomly between the limits. 3. Iteration count is set to one. 4. By placing all ‘n’ DG devices of each particle at respective candidate location, load flow analysis is performed. 5. Fitness value corresponding to each particle is evaluated: Fitness= PL –PL DG for maximum loss reduction. Initially all fitness is copied to pbest fitness. Maximum of pbest fitness gives gbest fitness which is a measure for maximum loss reduction, and the corresponding particle represents gbest particles. 6. New velocities for all the particles within the limits are calculated using eq (3). Particle positions are updated using eq (4). 7. Once the particles are updated, load flow analysis is performed, new fitness is calculated using eq (6) If new fitness is greater than pbest fitness then corresponding particle is moved to pbest particle. 8. Maximum of pbest fitness gives gbest fitness and corresponding particle is stored as gbest particle. 9. From pbest fitness, maximum fitness and average fitness values are calculated. Error is calculated using eq (8) Error = maximum fitness – average fitness …… (8) If this error is less than a specified tolerance, go to step 9. 10. Increment the current iteration count. If iteration count is not reached maximum, go to step 6. 11. Gbest fitness gives maximum loss reduction and gbest particle gives optimal DG size. III. SIMULATION The proposed method is tested on IEEE-14 bus system. Three DGs are considered. Three DGs will improve the power loss by 94.47% as compared to 70.41% for single DG. The results are shown in the following table.
  • 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME 24 Table 1: Summary of results for multi DG No.of DG Bus No. Power loss (p.u) DG size Voltage (p.u) Without DG With DG %loss reduction Without DG With DG % voltage improvement 1 DG 3 13.393 3.9635 70.41 141.77 1.01 1.04 2.97 2 DG 3 13.393 0.8673 93.52 96.7175 1.01 1.04 2.97 4 120.516 1.0183 1.0425 2.37 3 DG 3 13.393 0.7437 94.47 97.999 1.01 1.04 2.97 4 89.8149 1.0183 1.042 2.32 5 39.5512 1.02 1.038 1.85 Fig 4: Total real losses in 14 bus system before and after placement of DG Fig 5: Comparison of line losses before and after placement of DG
  • 6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME 25 Fig 6: Bus voltages before after placement of single DG Fig 7: Bus voltages before and after placement of two DGs 0 2 4 6 8 10 12 14 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 number of buses voltages before placement of DGs after placement of DGs 0 2 4 6 8 10 12 14 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 number of buses voltages before placement of DGs after placement of DGs
  • 7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 20-26 © IAEME 26 Fig 8: Bus voltages before and after placement of three DGs IV. CONCLUSION In this paper a particle swarm optimization method for optimal placement of multi DG is considered. There is a significant decrease in real and reactive power losses with multi DG placements. It can be seen that voltage profile is improved in using DGs. REFERENCES 1. Griffin T, Tomsovic K, Secrest D and Law A, “Placement of dispersed generations systems for reduced losses”, proceedings of the 33rd Annual Hawaii international conference on system sciences, No.2, pp, 1446-1454, 2000 2. S.Nagendra and A.Lakshmi Devi, “Power loss reduction and voltage improvement in transmission systems using Distributed generator units: A case study”, The ICFAI university Journal of Electrical & Electronics Engineering, vol.2, No.2, 2009. 3. A.M.EI-Zonkoly, “Optimal placement of multi distributed generation units including different load models using pso”, swarm and evolutionary computation, vol 1, pp-50-59, 2011. 4. Rajendra Prasad Payasi, Ashish K.Singh and Devender singh, “Review of DG planning, objectives, constraints, algorithms”, International journal of engineering science & Technology, vol.3, 2011. 5. W.EI-Hattam and M.M.A.Salama, “DG technologies, Definitions & benefits”, Electric power system research, vol.71, pp.119-128, 2002. 6. P. Sobha Rani and Dr. A. Lakshmi Devi, “Performance Improvement of Distribution System with Multi Distributed Generation using Particle Swarm Optimization”, International Journal of Electrical Engineering & Technology (IJEET), Volume 5, Issue 2, 2014, pp. 44 - 50, ISSN Print : 0976-6545, ISSN Online: 0976-6553, 0 2 4 6 8 10 12 14 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 number of buses voltages before placement of DGs after placement of DGs

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