Predicting Failures in Power Grids: The Case of Static Overloads

Mathematics – Optimization and Control

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

11 pages, 10 figures

Scientific paper

Here we develop an approach to predict power grid weak points, and specifically to efficiently identify the most probable failure modes in static load distribution for a given power network. This approach is applied to two examples: Guam's power system and also the IEEE RTS-96 system, both modeled within the static Direct Current power flow model. Our algorithm is a power network adaption of the worst configuration heuristics, originally developed to study low probability events in physics and failures in error-correction. One finding is that, if the normal operational mode of the grid is sufficiently healthy, the failure modes, also called instantons, are sufficiently sparse, i.e. the failures are caused by load fluctuations at only a few buses. The technique is useful for discovering weak links which are saturated at the instantons. It can also identify generators working at the capacity and generators under capacity, thus providing predictive capability for improving the reliability of any power network.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Predicting Failures in Power Grids: The Case of Static Overloads does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Predicting Failures in Power Grids: The Case of Static Overloads, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Predicting Failures in Power Grids: The Case of Static Overloads will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-513881

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.