Top Oil Temperature Prediction in Power Transformers Using Genetic Algorithm and Particle Swarm Optimization Method

Transformers are important and expensive elements of a power system. Inordinate localized temperature rise causes subsequent thermal breakdown. Obtaining appropriate model for top oil temperature (TOT) prediction is an important issue for dynamic and steady state loading of power transformers. There are many mathematical models which predict TOT. These mathematical models have many undefined coefficients which should be obtained from heat run test or fitting methods. In this paper, genetic algorithm (GA) and particle swarm optimization (PSO) are used to obtain these coefficients. The effects of mentioned optimization methods will be studied on improvement of adequacy, consistency and accuracy of the model. In addition these methods will be compared with the multiple linear regressions (MLR) to illustrate the improvement of the model.