Development and Optimization of Methods for Objective Interpretation of FRA Test Results

• Applicant: Professor Dr.-Ing. Stefan Tenbohlen
• Subject Area: Electrical Energy Systems, Power Management, Power Electronics, Electrical Machines and Drives
• Term: from 2017 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 380135324

Many of the transformers used in Germany are approaching the end of their projected service life. To ensure safe operation as long as possible and to prevent a failure, the condition of the insulation system has to be monitored. The evaluation of the transfer function is a comparative method for determining the mechanical state of the transformer windings. Transfer function assessment is carried out using the well-known frequency response analysis (FRA) technique. Previous studies have proved that the FRA can provide reliable information about the mechanical integrity of active parts inside the power transformer without any needs to dismantle the unit. They resulted in a standardized measuring procedure to make the measurements comparable with each other. However, one needs to make a decision about the transformer after the measurement, whether it is intact or needs to be repaired before further energization. This step is known as the FRA interpretation.However, the direct interpretation of the measurement results by a human being is subjective and therefore potentially prone to errors. The main goal of the current project is to develop, improve and optimize methods for objective interpretation of the FRA results. Different FRA interpretation methods are to be examined in power transformers. In order to test different interpretation techniques, a data set of FRA data with reference traces (fingerprint) and traces of known mechanical defects is necessary. FRA data for typical mechanical failure modes should be collected by means of data generation by high frequency transformer modeling, stepwise implementation of different mechanical defects in model windings and collection of real case data from the field from different utilities, diagnosis companies and working groups. Different numerical indices should be applied to these data sets and compared with each other in order to assess their applicability and ability to standardize the procedure of interpretation. As the final step, a study should be carried out to examine the possibility of defining thresholds for the selected indices. Additionally interpretation techniques based on artificial intelligence will be applied to the data sets in order to determine the type and extent of different faults.

• DFG Programme: Research Grants