Modelling the lumped parameter network of a power transformer using sweep frequency response analysis

ABSTRACT

Accurate modelling of power transformers is beneficial for fault diagnosis and the health condition assessment of the power transformers. However, measuring the internal parameters of power transformers while they are in operation is not feasible due to the costs involved in interrupting the power supply and dismantling the components. To address this issue, this paper proposes a new method that uses artificial neural networks and genetic algorithms to synthesise the power transformer lumped parameter network based on the frequency response. The proposed methodology can be utilised to model power transformers and obtain lumped parameter values with minimal cost and time. A physically realisable transfer function derived using the sweep frequency response test data of a power transformer is the input of the proposed model. The combined approach of an artificial neural network and genetic algorithms generates the output as resistive, inductive, and capacitive components of the lumped parameter circuit of power transformers. Furthermore, the entire approach is explained using a case study and validated by means of error analysis. The proposed method is an easy-to-implement process to develop in a computer program that can be used effectively for accurate fault identification in power transformers and health assessments.

KEYWORDS:

• Artificial neural networks
• genetic algorithm
• lumped parameter equivalent circuit
• power transformer
• sweep frequency-response analysis
• transfer function

Nomenclature

$R_i$	=	Resistance of the winding in $i^{th}$ section $\left(\mathrm{\Omega }\right)$
$L_i$	=	Self-inductance of the winding in $i^{th}$ section (H)
$C{s}_i$	=	Series capacitance of the winding in $i^{th}$section (F)
$C{g}_i$	=	Ground capacitance in $i^{th}$section (F)
$Rc{s}_i$	=	Insulation resistance (parallel to windings) in $i^{th}$section $\left(\mathrm{\Omega }\right)$
$M_{i,j}$	=	Sectional mutual inductance of $i^{th}$and $j^{th}$ sections (H)
$n$	=	Number of sections in lumped parameter network
fit_fcn	=	Objective function of genetic algorithm
constraint_fcn	=	Constraint function of genetic algorithm
Fitt_val	=	Fitness value equation of genetic algorithm

Disclosure statement

No potential conflict of interest was reported by the authors.