Case study on thermal optimization of oil immersed transformer used in solar power plant based on genetic algorithm and computational fluid dynamics

Abstract

Transformers are one of the most capital investments in the solar power generation. Their safe and stable operations in the electrical networks are important. The main failure factor of transformers is the high temperature generated by the losses during operation, which increases the probability of insulation damage that significantly affects the useful life of transformer. Considering the importance of oil temperature and its effects on the life of the transformer, a numerical method is developed in this paper to optimize the cooling system of the transformer. In this regard, genetic algorithm is used as an optimization method to minimize the total cost of the cooling system while maintaining the required thermal conditions of the transformer. A comprehensive parametric study is carried out among the effective cooling geometry parameters using 3-D electromagnetic and thermal models of the photovoltaic transformer to evaluate and analyze the temperature distribution. The accuracy and feasibility of the proposed method is established by comparing the numerical results with those obtained from the experimental test. The results of the proposed method are found to be in a good agreement with the experimental and simulation results.