Weight Optimization of Oil Type Transformer with Crayfish Optimization Algorithm

Transformers used in the transmission and distribution of electricity are electrical machines that ensure the transmission of electricity at constant power and frequency by using magnetic field strength. In this study, weight optimization of oil-type power and distribution-type transformers in different power levels (50kVA and 100kVA) was calculated using the Crayfish Optimization Algorithm (COA). The purpose of the study is to perform weight optimization and calculate weight reduction. The variable parameters used as current density value (s) and iron section suitability value (C) were determined and weight optimization was calculated. A detailed population size analysis (for ten different population values) and maximum iteration analysis (for four different maximum iteration values) were performed on the weight optimization problem of transformers with COA. The effects of changing population sizes and maximum iteration numbers on the performance of COA were shown. The results obtained were analyzed in detail by comparing them with other studies in the literature (GWO, FA, OOA, and ZOA). While the transformer iron weight calculated with the traditional approach with COA is further minimized, the efficiency is further maximized. When the comparison results are examined for 50kVA and 100kVA, while COA increases the efficiency of transformers better than old heuristic methods such as GWO and FA, it could not minimize the iron weight. It was also observed that the C and s variable values were similar in all three algorithms (COA, GWO, and FA). When the COA, ZOA, and OOA algorithm results are examined for 50kVA and 100kVA, the heuristic algorithm that finds the minimum total iron weights is COA, while the highest efficiency is again achieved by COA. COA's transformer total iron weight results were consistent with the traditional, ZOA and OOA algorithms, but not with GWO and FA. In addition, the transformer efficiency calculated depending on the iron weight showed the best performance with COA among the comparison algorithms. This study has shown that transformer weight can be reduced and efficiency can be increased by using intuitive methods. The solution to the transformer iron weight calculation problem with COA is a first in the literature and the obtained results were introduced to the literature.