Abstract
Power transformer outages have a considerable economic impact on the operation of an electrical network. Obtaining appropriate model for power transformer top oil temperature (TOT) prediction is an important topic 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. Therefore, a code has been provided under MATLAB software. 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 Regression (M-L R) to illustrate the improvement of the model.
Highlights
Large power transformers are the most valuable assets in electrical power networks
There are many mathematical models which predict top oil temperature (TOT). These mathematical models have many undefined coefficients which should be obtained from heat run test or fitting methods
In addition these methods will be compared with the Multiple-Linear Regression (M-L R) to illustrate the improvement of the model
Summary
Large power transformers are the most valuable assets in electrical power networks. In order to improve transformer utilization without thermal criteria violation such as top oil temperature (TOT), and hottest spot temperature (HST), TOT and HST need to be predicted accurately in dynamic loading of transformer and maximum steady state loading (SSLmax) [1,2]. Accurate TOT and HST prediction allows system planners to plan optimally for transformer purchases. Some mathematical models are introduced for predicting TOT Undefined coefficients of these mathematical models can be obtained from heat run experiment or fitting methods through experimental data such as multiple linear regression method and optimization methods like PSO and GA which will be studied in this paper. GA and PSO are used so to define coefficients of models through experimental data. One of the main challenges of power transformers thermal modeling is the instability of obtained coefficients from similar experimental data.
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