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Power Transformer Top Oil Temperature Estimation with GA and PSO Methods

Author(s): Mohammad Ali Taghikhani

Journal: Advances in Molecular Imaging
ISSN 2161-6728

Volume: 04;
Issue: 01;
Start page: 41;
Date: 2012;
Original page

Keywords: Top-Oil Temperature (TOT) | Genetic Algorithm (GA) | Particle Swarm Optimization (PSO) | Multiple Linear Regression (M-L R)

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.
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Tango Jona
Tangokurs Rapperswil-Jona