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Optimal Distributed Generation Placement for Maximum Loss Reduction using Teaching Learning Based Optimization through Matlab GUI

M.Harini*, M. Damodar Reddy**

* Student, EEE Department, SV University College of Engineering, Andhra Pradesh, India.

** Professor, EEE Department, SV University College of Engineering, Andhra Pradesh, India.

Periodicity:November - January'2015
DOI : https://doi.org/10.26634/jps.2.4.3112

Abstract

In distribution systems, distributed generation (DG) plays a vital role in meeting the demands of electrical energy. It is a small and self generating plant which provides power to industries and homes by improving the voltage profile, stability and by reducing the system losses. Optimal location and sizing of distributed generation in a radial distribution network is an optimization problem with both discrete and continuous variables. This paper presents a fuzzy approach for finding optimal locations and Teaching Learning Based Optimization (TLBO) technique for finding optimal DG sizes. The main objective is to reduce the system power losses and improve the voltage profile of the distribution network. The TLBO is inspired by the influence of the teaching by the teacher on the students in a class. In this paper, GUI is developed in Matlab to perform load flow and to obtain optimal location and sizing of DG. The proposed method was tested on IEEE 15 bus, 33 bus and 69 bus systems and the results were discussed.

Keywords

Distributed Generation, Fuzzy Approach, Teaching Learning Based Optimization, Voltage Profile, Loss Reduction, Graphical User Interface.

How to Cite this Article?

Harini, M., and Reddy, M. D. (2015). Optimal Distributed Generation Placement For Maximum Loss Reduction Using Teaching Learning Based Optimization Through Matlab Gui. i-manager’s Journal on Power Systems Engineering, 2(4), 21-28. https://doi.org/10.26634/jps.2.4.3112

References

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[3]. Caisheng Wang & M. Hashem Nehrir (2004). “Analytical Approaches for optimal placement of Distributed Generation sources in Power Systems”, IEEE Transactions on Power Systems, Vol 19.

[4]. Damodar Reddy, M & Veera Reddy, V.C (2008). “Optimal Capacitor Placement using fuzzy and particle swarm optimization method for maximum annual savings”, APRN Journal of Engineering and Applied Sciences, Vol 3.

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[8].N. Khalesi , Nazkhanom Rezaei & Mahmoud Reza Haghifam (2011). “DG allocation with application of dynamic programming for loss reduction and reliability Improvement” International Journal of Electrical Power and Energy systems, Vol 33,issue 2, pp.288-295.

[9]. Mohammad Hasan Moradi & Mohammad Abedini Ghosh (2012). “A combination of genetic algorithm and Particle swarm optimization for optimal DG location and sizing in distribution systems” International Journal of Electrical Power and Energy systems, Vol 32, issue 8, pp.66-74.

[10].Ravipudivenkata Rao, Vimal J. Savsani & D.P.Vakharia (2012). “Teaching Learning Based Optimization: An optimization method for continuous non-linear large scale problems” International Journal of Electrical Power and Energy systems, Vol.183, issue 1, pp.1-15.

[11]. Jaikaran Singh, Mukesh Tiwan & Manan (2014). “Capacitor Placement in Radial Distribution System Using Ant Colony Search Algorithm”, IJETT, Vol 9.

[12].Sneha Sultana & Provas Kumar Roy (2014). “Optimal Capacitor Placement in Radial Distribution System Using Teaching Learning Based Optimizatio Algorithm”, Electrical power and Energy Systems, Science Direct, pp.387-398.

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Optimal Distributed Generation Placement for Maximum Loss Reduction using Teaching Learning Based Optimization through Matlab GUI

Abstract

Keywords

How to Cite this Article?

References

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