Intelligent, sensor-based condition monitoring of transformer stations in the distribution network

Today’s maintenance and renewal planning in transformer stations of energy distribution networks is mainly based on expert knowledge, experience gained from historical data as well as the knowledge gathered from regular on-site inspections. This approach is already reaching its limits due to insufficient databases and almost no information about the stations’ condition being gathered between inspection intervals. A condition-based strategy that requires more maintenance for equipment with a high probability of failure is needed. Great potential is promised by intelligent sensor-based diagnostics, where objective comparability can be achieved by condition monitoring of the station fleet. Cost-effective micro-electromechanical (MEMS)-bases sensor systems promise to provide a suitable solution for network operators and enable a widespread use. In our paper, we present a MEMS-based sensor system, that can be used to gain information about network transparency, station safety as well as maintenance and renewal planning. Moreover, we propose an intelligent measurement scheme which adaptively selects relevant data and avoids unneeded redundancy (Smart Data instead of Big Data).