Self-Assessment of Health Conditions of Electrical Assets and Grid Components: A Contribution to Smart Grids

Abstract

Broadening the concept of smart grid to include bi-directional interactions between grid and asset electrical components as regards their health conditions would help optimizing maintenance and operation actions from asset/grid management side. However, as long as data processing from diagnostic tools requires long and costly expert time, there is no real chance that the knowledge and technology developed in the field of condition monitoring can become broadly available for grid smart management. This article presents algorithms able to automate the acquisition of health information from electrical asset components. Referring to partial discharges, which are associated with the most harmful insulation aging factor, an innovative technique able to reject noise and thus make automatic partial discharge recognition possible, is proposed and its validity assessed. The information from partial discharge measurement, and other diagnostic quantity monitored is translated into a simple and straightforward indication of component health. This assessment can be automatic, dynamic and continuously updating during operation, thus making feasible an effective time-based condition maintenance approach. The outcome of the proposed approach is to contribute keeping asset reliability at a specified level, minimizing disastrous outages and increasing the return of investment. This benefits also optimal electrical component operation and power flow.