K. SriKumar,




Load flow,forward-backward sweep method,loss factors analysis,Voltage sensitivity factors,Cognitive component,Weighted factor,Bird Swarm optimisation,Distributed generation,Optimal location,Optimal size,Real Power losses,Size Tuner,


In this paper a natural habitat inspired metaheuristic Bird Swarm Optimization algorithm is implemented with improvisations made for the development of solution for the optimal allocations and optimal sizeprediction problem of Dispersed generation/ Distributed Generator in a radial power system distribution system in consideration of the drawbacks in the previous algorithms both in the context of convergence time and the optimal sizing with respect to the cost analysis for operation of the system with different number of DG’s installed in such a way that the optimal locations and sizes of DG’s installed is finalised with highest priority to the economical operation along with the immediate priority given to the network losses along with voltage deviations. To avoid the draw backs in previous optimisation algorithm regarding accuracy and run time. Along with the Cognitive component Weighted factor Bird Swarm optimisation (CWFBSO) algorithm a new concept is introduced called DG Size tuner Such that cost effective economical installation is possible as by the size tuner it is possible to compare the losses and voltage profile within the mean difference of the optimal sizes of final allocation determined by the main algorithm i.e., CWFBSO. Obtained results using CWFBSO in determining optimal locations and sizes of DG’s is capable showing good performance with less run time and convergence time and by using size tuner the optimal size selected economically with respect to less voltage deviation and minimal losses.


I. A. Alsaadi, B. Gholami, “An effecctive approach for distribution system
power flow solution”, Intern. Journ. of Electr. and Electron. Eng., 2009 .
II. A. M. Imran, M. Kowsalya, “Optimal size and siting of multiple distribution
generators in distribution system using bacterial foraging optimization”,
Swarm and Evolut. Comput., Vol.: 15, pp. 58-65, 2014.
III. C. V. Suresh, M. S. Giridhar, “Analysing the effect of distributed generators
on economical and technical aspects of distributed systems”.
IV. J. Kennedy, R. C. Eberhart, “Particle swarm optimization”, Proceedings of
the IEEE International Conference on Neural Networks IV, Piscataway, NJ:
IEEE Service Center, pp. 1942-1948, 1995.
V. J. Federico, V. Gonzalez, C. Lyra, “Learning classifiers shape reactive power
to decrease losses in power distribution networks,” Proc. IEEE Power Eng.
Soc. General Meet., Vol.: 1, pp. 557–562, 2005.
VI. L. d. S. Coelho, V. C. Mariani, “Particle Swarm Optimization with Quasi-
Newton Local Search for Solving Economic Dispatch Problem”, IEEEInternational Conference on Systems, Man, and Cybernetics, Taipei, Taiwan,
VII. M. N. Moradi, M. Abedini, “A combination of genetic algorithm and particle
swarm optimization for optimal DG location and sizing in distribution
systems”, Elsevier, Science Direct, Electr. Power Energy Syst., pp. 66-74,
VIII. M. Z. A. C. Wanik, A. Mohamed, “Intelligent management of distributed
generators for loss minimization and voltage control ”,15th Intenational
(MELCON), pp. 685-690, 2010.
IX. N. Acharya, P. Mahat, N. Mithulananthan, “An analytical approach for
distributed generation allocation in distribution network ”, Electr. Power
and Energy Systems, pp. 669-678, 2006.
X. P. S. Georgilakis, , N. D. Hatziargyriou, “Optimal distributed generation
placement on power distribution networks: models, methods, and future
research’ , IEEE Trans. Power Syst, Vol.: 28, Issue: 3, pp. 3420-3428.,2013
XI. R. C. Eberhart, J. Kennedy, “A new optimizer using particles swarm theory”,
Proceedings of the 6th In-ternational Symposium on Micro Machine and
Human Science, Vol.: 4, Issue: 6, pp. 39-43, 1995.
XII. R. Jabr, B. Pal, “Ordinal optimisation approach for locating and sizing
distributed generation”, IET Gener, transm. Distrib., 2009, Vol.: 3, Issue: 8,
pp. 713-723
XIII. R. K. Singh, S. K. Gowsami, “Optimum allocation of distributed generations
based on nodal pricing for profit, loss reduction, and voltage improvement
including voltage rise issue”, Elsevier, Science Direct, Electr. Power Energy
Syst., Vol.: 32, pp. 637–644, 2010.
XIV. S. K. Injeti, N. P. Kumar, “A Novel Approach to Identify Optimal Access
Point and Capacity of Multiple DGs in a Small, Medium and Large Scale
Radial Distribution Systems”, Elsevier, Science Direct, Electr. Power Energy
Syst., pp. 142-151, 2013.
XV. S. K. Injeti, V. K. Thunuguntla, M. Shareef, “Optimal allocation of capacitor
banks in radial distribution systems for minimization of real power loss and
maximization of network savings using bio-inspired optimization
algorithms”, Electrical Power and Energy Systems, Vol.: 69, pp. 441–455,
XVI. Y. A. Katsigiannis, P. S. Georgilakis, “Effect of customer worth of
interrupted supply on the optimal design of small isolated power systems
with increased renewable energy penetration’. IET Gener. Transm. Disturb.,
7, (3), pp.,265-275, 2013.

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