PD Source Location Utilizing Acoustic TDOA Signals in Power Transformer by Fuzzy Adaptive Particle Swarm Optimization

Abstract

Acoustic Emission Partial discharge (AEPD) Location is one of the techniques utilized by many transformer manufacturers and power utility engineers in routine and critical situation for optimal operation of the electrical power system as well as further risk management and repair planning. The PD detection is not enough to take solution so identification of PD source is essential to restore apparatus condition. This work aim is to localize the defect geometrically by means of time difference of arrival signals in order to accurately locate PD source in the power transformer. The solution for this PD source location is acquired by making these nonlinear equations as optimization problem. In the present work, the fuzzy adaptive particle swarm optimization (FAPSO) for partial discharge (PD) source localization in power transformer is proposed. In this technique, the inertia weight is effectively regulated by using conditional IF-THEN statements to improve the global optimal solution and impairs the local convergence problem and improves the accuracy in estimating the PD source location. The simulation results reveals that PD location accuracy with minimum of maximum deviation error, absolute error and relative error is better when compared to other constant parameter intelligent methods which were reported in the literature.