A comparative analysis of multi-objective and multialgorithm approaches for the optimal design of distribution transformers

UBIOMO EMMANUEL UBEKU, FRIDAY OSASERE ODIASE

Abstract


This paper presents the sizing of three phase transformer using four intelligentalgorithms namely geometric programming, genetic algorithm, simulated annealing,and particle swam optimization. Four independent objective functions and eightconstraints were used. The comparative analysis carried out on the results obtainedfrom these intelligent algorithms shows that all the outputs from the intelligentalgorithms are the same. The fastness of results shows that geometric programmingis the fastest, while genetic algorithm, simulated annealing, and particle swamoptimization followed in that order. The output results from the cost objective functionwere compared with the results obtained by Masood (2012) and it showed that moneywas saved in the following order, 6.4%, 16.32%, 10.63% and 16.79% respectively forgeometric programming, genetic algorithm, simulated annealing, and particle swamoptimization.


Keywords


Constraints; minimisation; objective functions; particle swam optimization; simulated annealing

Full Text:

PDF

References


Amit Kr. Yadav, Rahi, O.P, Hasmat Malik & Abdul Azeem. 2011. Design Optimization of High-

Frequency Power Transformer by Genetic Algorithm and Simulated Annealing. IJECE 1(2):

-109.

Boyd, S., Kim. S., Vandenberghe, I. & Hassibi, A. 2007. A tutorial on geometric programming. Optim

Eng 8:67-127.

Jabbr, R.A. 2005. Application of geometric programming to transformer design. Magnetics, IEEE

Transactions on, 41(11): 4261-4269.

Masood, A. M., Jabbr, R.A., Masoum, M.A.S., Junaid, M. & Ammar, A. 2012. An Innovative Technique

for Design Optimization of Core type 3-phase Transformer using Mathematica. GJTO 3: 30-35.

Mohammad A. Alsaffar. & Mohamed A. El-Sayed. 2014. Emission constrained unit commitment

of Kuwait power generation system using genetic algorithm. Journal of Engg. Research, 2(2):

-121.

Ravi Kiran, V., Manoj, V. & Praveen Kumar, P. 2013. Genetic Algorithm approach to find excitation

capacitances for 3-phase smseig operating single phase loads. Carib.j.SciTech, 1: 105-115.

Sendilkumar, S., Mathur, B.L. & Mohammed Imran. 2013. Discrimination of power transformer inrush

and internal fault current using time to time transform and fault classification using fuzzy clustering.

Journal of Engg. Research, 1(3): 87-108.

Tsili, M.A., Kladas, A.G., Tsivgouli, A.J., Georgilakis, P.S., Souflaris, A.T & Paparigas, D.G.

Efficient finite element model for power transformer optimization. Proc. 15 Inter Conf on the

Computation of Electromagnetic (COMPUMAG) Shenyang, China.

X.-S. Yang. 2010. Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Inspired

Computation, 2(2): 78-84.


Refbacks

  • There are currently no refbacks.