Breakers’ state estimation using autoassociative neural networks

This work addresses the breakers’ state estimation in power systems. A classification methodology is applied, which considers the competition of two autoencoders, each one trained to learn a specific manifold concerning a breaker’s status: “open” and “closed”. The classification decision is made with the adoption of the state referring to the autoencoder with the lowest error. The methodology was applied for 11 breakers, under two modes: the first includes, for the training of the autoencoders, all information that is immediately adjacent to the breaker and its buses, the second excludes information concerning the flows directly connected to the respective breaker. The first technique returned performances that can be considered equivalent to the empirical approach. The second approach tested (without direct flows), represents a significant gain: without information on direct flows, the empirical approach is unfeasible.