A multi-hypothesis sequential probability test for partial discharges localization in power transformers

We present a new method to accurately locate partial discharge by using a sequential fault detection and identification (FDI) algorithm for detecting a bias fault in the measurements of partial discharge in transformer insulation system using acoustic signals. In this paper, a novel technique is proposed to identify the possibility of measurement errors generated from acoustic emission sensors during partial discharge localization inside a transformer tank. The technique probabilistically detects and identifies possible bias on the sensors' measurement. This bias is possibly caused by sensor's fault, sensor's aging, or proximity of the PD to a certain sensor in comparison to other sensors. Correct detection of sensors' measurement bias enables high accuracy PD localization as will be demonstrated in this paper. The accuracy and convergence characteristics of the proposed algorithm are verified in a simulation environment. This study is tremendously important for scheduling and starting maintenance/repair actions cost and time efficiently or to perform a risk analysis.