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 mixedinteger 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.

Applications of Novel Heuristic Algorithms in Design Optimization of Energy-Efficient Distribution Transformer

“…The most recent trend in transformer design is the use of heuristic optimization algorithms with TOC as the objective function. However Finite Element Analysis (FEA) is mainly used for transformer losses calculations and design validation combined with heuristic algorithms [3][4][5][6][7][8][9][10][11][12][13][14]. In [3], five nature-inspired optimization algorithms are used to minimize the shell type distribution transformer's main material cost.…”

“…However Finite Element Analysis (FEA) is mainly used for transformer losses calculations and design validation combined with heuristic algorithms [3][4][5][6][7][8][9][10][11][12][13][14]. In [3], five nature-inspired optimization algorithms are used to minimize the shell type distribution transformer's main material cost. The authors have used Artificial Bee Colony (ABC), Backtracking Search Optimization Algorithm (BSOA), Competitive-Adaptive Differential Evolution Algorithm, Cuckoo Search (CS) algorithm, and Flower Pollination Algorithm (FPA).…”

FEM-based Modeling and Optimization of Dry-Type Transformers with Metaheuristic Algorithms

“…However, since changes in core size affect the magnetic characteristic, the change in magnetic flux density is also a parameter to be considered. Considering this situation, in the study conducted in [7], eight variables, including the current density, were determined for the minimum cost optimization of the oil-type transformer. These parameters were optimized and compared using methods such as the Artificial bee colony (ABC) algorithm, Backtracking search optimization (BSA) algorithm, Cuckoo search (CS) algorithm, Flower pollination algorithm (FPA), and Competitive-Adaptive Differential Evolution algorithm (b6e6rl).…”