An Algorithm for Fast Fault Location and Classification Based on Mathematical Morphology and Machine Learning
Felipe Wilches‐Bernal, Miguel Jimenez Aparicio, Matthew J. Reno
Abstract
This paper presents a novel approach for fault location and classification based on combining mathematical morphology (MM) with Random Forests (RF). The MM stage of the method is used to pre-process voltage and current data. Signal vector norms on the output signals of the MM stage are then used as the input features for a RF machine learning classifier and regressor. The data used as input for the proposed approach comprises only a window of 50 µs before and after the fault is detected. The proposed method is tested with noisy data from a small simulated system. These results show 100% accuracy for the classification task and prediction errors with an average of ∼13 m in the fault location task.