Metaheuristic Algorithms Based Approaches For Efficiency Analysis Of Three-Phase Dry-Type Transformers

Abstract

Transformers are considered as the significant contributors to the efficient transmission and distribution of electrical energy. The ability to change the voltage and current levels in inverse proportion help to reduce the conductor losses. However, today’s stringent requirements for more significant efficiency markings turn attention to the efficiency of individual components in a power system. Therefore, a great deal of effort is being placed to maximize the efficiency of the transformers without compromising their fundamental function. This is a complex problem and requires the use of advanced design tools. Metaheuristic methods developed in recent years are being used in electrical engineering, where they provide savings in design time and great success in finding the optimum solution. In this study, we have used the Particle Swarm Optimization (PSO), the Simulated Annealing (SA), and the Tree Seed Algorithm (TSA) methods, respectively. The objective is to develop a design methodology for threephase dry-type transformers and to maximize their efficiency. The results of the three algorithms are compared to validate the optimum solution. For the demonstration of the process, a three-phase 100 kVA dry-type transformer is used. After the mathematical model of the transformer is created, the transformer parameters, current density (s), and transformer iron cross-section acceptability (C) are optimized. As a result, it has been observed that the efficiency of transformers can be increased beyond what is achieved with conventional techniques. The efficiency has been optimized and increased from 97.5% to 98.44%.