Estimation of Transformer Insulation Properties Under Different Nanoparticle Concentrations Using Curve Fitting Methodology

Mineral oil based nanofluids used in transformers are recognized to have improved electrical and physical properties compared to conventionally used liquid insulation. Silica nanoparticle-based nanofluids (SNF) are also a candidate for the transformer insulating liquid. In this study, curve-fitting techniques are used to estimate the oil properties for different concentrations of nanoparticles. Firstly, samples of the SNF are synthesized using the conventional two-step procedure. The values of dielectric breakdown voltage (BDV) and interfacial tension (IFT) for different concentrations of SiO
2
in SNF are obtained using experimentation. Various curve fitting models are developed considering the concentration of SiO
2
as input, and BDV and IFT as outputs. Here, curve fitting methodologies such as Exponential fit, Polynomial fit, Gaussian fit, Fourier fit, and Sum of sine fit are used. The developed model can be used to estimate BDV and IFT for any concentration of SiO
2