Detection and Evaluation of Partial Discharges in Power Transformers with Special Sensors and Diagnostic Methods

Abstract

Transformers are key elements of the power grid. Due to their importance and high initial cost, asset managers utilize monitoring and diagnostic tools to optimize their operation and extend their service life. The main objective of this thesis is to develop new methods in the field of monitoring and diagnosis of transformers in order to reduce maintenance costs and decrease the frequency of forced outages. For this purpose, two concepts are proposed.

Small generator step-up transformers are essential in wind and photovoltaic parks. The first presented concept entails an online fault gas monitoring system for these transformers, specially hermetically-sealed transformers. The developed compact, maintenance-free and cost-effective monitoring system continuously tracks the level of the key leading indicators of transformer faults in the gas cushion.

The second presented concept revolves around partial discharge (PD) assessment by the UHF measurement technique, which is based on capturing the electromagnetic (EM) waves emitted in case of PD in the insulation of a transformer. In this context, the complex EM system established when probes are introduced into the tank of a transformer and with PD as the excitation source is analyzed. Drawing on this foundation, a practical approach to the detection and classification of PD with the focus on the selection of the optimal frequency range for performing UHF measurements depending on the device under test is presented. The UHF measurement technique also offers the possibility of PD localization. Here, the determined arrival time (AT) of the captured signals is critical. A PD localization algorithm, based on a multi-data-set approach with a novel AT determination method, is proposed. The methods and algorithms proposed for the detection, classification and localization of PD are validated by means of practical experiments.