Additional information from voltage dips

This paper presents some methods to extract additional information from voltage dip recordings, beyond residual voltage and duration. Additionally it discusses some issues related to the massive amount of data obtained from modern measurements that, is referred to as Big Data. The paper proposes some Deep Learning based algorithms as good candidates to extract complex features from big data as a step towards additional information. The applications of the information include predicting individual equipment performance, fault type and location, protection operation, and overall load behavior. Individual equipment and overall load include production as well as consumption.