Learning to infer: A new variational inference approach for power grid topology identification

Identifying arbitrary topologies of power networks is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. A new variational inference approach is developed for efficient marginal inference of every line status in the network. Optimizing the variational model is transformed to and solved as a discriminative learning problem. A major advantage of the developed learning based approach is that the labeled data used for learning can be generated in an arbitrarily large amount at very little cost. As a result, the power of offline training is fully exploited to offer effective real-time topology identification. The proposed methods are evaluated in the IEEE 30-bus system. With relatively simple variational models and only an undercomplete measurement set, the proposed method already achieves very good performance in identifying arbitrary power network topologies.