Abstract
Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hypotheses grows exponentially with the network size. A new 'Learning-to-Infer' method is developed for efficient inference of every line status in the network. Optimizing the line outage detector is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learning-to-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount rapidly and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time multi-line outage identification. The proposed methods are evaluated in the IEEE 30, 118, and 300 bus systems. Excellent performance in identifying multi-line outages in real time is achieved with a reasonably small amount of data.
Original language | English (US) |
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Article number | 8747534 |
Pages (from-to) | 555-564 |
Number of pages | 10 |
Journal | IEEE Transactions on Smart Grid |
Volume | 11 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2020 |
All Science Journal Classification (ASJC) codes
- General Computer Science
Keywords
- Line outage detection
- Monte Carlo method
- machine learning
- power system monitoring
- variational inference