Partial discharge detection in transformer using adaptive grey wolf optimizer based acoustic emission technique

Partial discharge (PD) occurring in the insulation systems of the transformer is an important indicator of their deterioration. Insulation degradation is a well-known source of power transformer failure. Many methods have been realized for detection and localization of PD source in the transformer. In this paper sensor based acoustic emission technique has been implemented for PD detection. To repair site of PD after detection it is very important to find the exact location of PD sources in the equipment. This paper proposed adaptive grey wolf optimizer (AGWO) algorithm for localization of PD source using acoustic emission technique. A novel bio-inspired optimization algorithm based on the hunting process of wolves in nature called the grey wolf optimizer (GWO) Algorithm. In contrast to meta-heuristics; the main feature is randomization having a relevant role in both exploration and exploitation in the optimization problem. A novel randomization technique termed adaptive technique is integrated with GWO and exercised on unconstrained test benchmark function and optimum location of PD in the transformer. Integration of new randomization adaptive technique provides potential to AGWO algorithm to attain global optimal solution and faster convergence with less parameter dependency. AGWO solutions are evaluated and a result shows it's competitively better performance over other optimization algorithms.