Design optimization of distribution transformers with nature-inspired metaheuristics: a comparative analysis


Abstract: Many economies in the world have adopted energy-efficiency requirements or incentive programs mandating or promoting the use of energy-efficient transformers. On the other hand, increases in transformer efficiency are subject to increases in transformer weight and size, sometimes as much as 50% or more. The transformer manufacturing industry is therefore faced with the challenge to develop truly optimum designs. Transformer design optimization (TDO) is a mixed-integer nonlinear programming problem having a complex and discontinuous objective function and constraints, with the objective of detailed calculation of the characteristics of a transformer based on national and/or international standards and transformer user requirements, using available materials and manufacturing processes, to minimize manufacturing cost or total owning cost while maximizing operating performance. This paper gives a detailed comparative analysis of the application of five modern nature-inspired metaheuristic optimization algorithms for the solution of the TDO problem, demonstrated on three test cases, and proposes two algorithms, for which it has been verified that they possess guaranteed global convergence properties in spite of their inherent stochastic nature. A pragmatic benchmarking scheme is used for comparison of the algorithms. It is expected that the use of these two algorithms would have a significant contribution to the reduction of the design and manufacturing costs of transformers.

Keywords: Distribution transformer, transformer design optimization, high efficiency, metaheuristics, swarm intelligence, differential evolution

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