Online detection of partial discharge inside power transformer winding through IFRA

Predictive maintenance offers substantial benefits for detecting the early signs of power transformer faults before they burgeon into catastrophic failures. Online impulse frequency response analysis is a recently-developed diagnostic method for in service transformer with a promising outlook. This paper aims to propose an online partial discharge detection method the online IFRA test. To emulate the dynamic performance characteristics of in service transformer, 3D finite element model of the transformer is calculated in Maxwell Software. In post processing, the FEM sub-circuit model is exported into an external Maxwell Spice circuit to study the terminal behaviors of the transformer. A pulse signal simulating PD is injected between sections of the LV winding. The S transform is then applied to the recorded input and output signals in healthy and faulty conditions to construct the electrical impedance as well as the time-frequency contours of the transient responses. Also, a mechanical deformation is imposed on the transformer to compare its impact on online IFRA to the impact of internal partial discharge