Optimization of Transformer Design using Bacterial Foraging Algorithm

are widely used in electric power system to perform the primary functions, such as voltage transformation and isolation. So the transformer design is emphasize. In this paper, a transformer design optimization method is proposed aiming at designing the transformer to optimize the efficiency and cost. The design optimization of transformer is formulated as unconstrained non linear multivariable programming technique. Five independent variables and three constraints are taken to meet the requirement of the design. A heuristic search technique Bacterial Foraging Algorithm (BFA) is used to solve the optimization problem. The effectiveness of the proposed approach has been tested with two sample transformers and the simulation results are compared against with the conventional method, Simulated Annealing (SA) technique and Particle Swarm Optimization (PSO) method. The simulation results reveal that the proposed method determines the optimal variables of transformer long with the performance parameters efficiently and accurately. Keyworddesign optimization, cost, efficiency, bacterial

[1]  R. Bhuvaneswari,et al.  Optimization of Single-phase Induction Motor Design using Radial Basis Function Network , 2005, 2005 Annual IEEE India Conference - Indicon.

[2]  Pavlos S. Georgilakis,et al.  A heuristic solution to the transformer manufacturing cost optimization problem , 2007 .

[3]  Zbigniew Michalewicz,et al.  An evolutionary algorithm for the optimal design of induction motors , 1998 .

[4]  S. Mishra Bacteria foraging based solution to optimize both real power loss and voltage stability limit , 2007, 2007 IEEE Power Engineering Society General Meeting.

[5]  P. A. Abetti,et al.  Application of Digital Computers to Transformer Design [includes discussion] , 1956, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

[6]  G. Panda,et al.  Bacteria Foraging Based Independent Component Analysis , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[7]  Gabriel Noriega,et al.  On line parameter estimation of electric systems using the Bacterial Foraging algorithm , 2009, 2009 13th European Conference on Power Electronics and Applications.

[8]  Q. Henry Wu,et al.  Bacterial Foraging Algorithm For Dynamic Environments , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[9]  O. W. Andersen Optimum Design of Electrical Machines , 1967 .

[10]  Stefanos Kollias,et al.  A novel iron loss reduction technique for distribution transformers based on a combined genetic algorithm - neural network approach , 2001 .

[11]  Bijaya Ketan Panigrahi,et al.  Bacterial foraging optimisation: Nelder-Mead hybrid algorithm for economic load dispatch , 2008 .

[12]  P. Odessey Transformer Design by Computer , 1974 .

[13]  S. Subramanian,et al.  Optimal Design of Power Transformer Using Simulated Annealing Technique , 2006, 2006 IEEE International Conference on Industrial Technology.

[14]  Anthony John Moses,et al.  Problems in the design of power transformers , 1974 .

[15]  Ion Boldea,et al.  Induction motor electromagnetic design optimization: Hooke Jeeves method versus genetic algorithms , 2010, 2010 12th International Conference on Optimization of Electrical and Electronic Equipment.