Parameter identification of transformer detailed model based on chaos optimisation algorithm

The R-L-C-M model of a power transformer is obtained from geometrical structure and is extremely appropriate for studying transient phenomena in a transformer and detecting mechanical faults. The precision of this model depends strongly on the precision of its parameters. The accuracy of these parameters that are calculated by analytical formulae is limited because of different reasons. In this study a chaos optimisation algorithm (COA) is introduced as a method to identify the parameters of the R-L-C-M model, which represents the transient behaviour of a power transformer more accurately than the model based on calculated parameters using analytical formulae. By applying an experimental test on a proper test object, not only are the validity and accuracy of the proposed method verified, but also COA is compared with another optimisation method referred to as real code genetic algorithm (RCGA).