Shape Preserving Incremental Learning for Power Systems Fault Detection

Jose Cordova, Carlos Soto, Mostafa Gilanifar, Yuxun Zhou, Anuj Srivastava, Reza Arghandeh

Abstract: This letter presents a shape preserving incremental learning algorithm that employs a novel shape-based metric called the Fisher-Rao amplitude-phase distance (FRAPD) metric. The combined amplitude and phase distance metric is achieved on a function space from the Fisher-Rao elastic registration. We utilize an exhaustive search method for selecting the optimal parameter that captures the amplitude and phase distance contribution in FRAPD when performing a clustering process. The proposed incremental learning structure based on the shape preserving FRAPD distance metric utilizes continuously updated fault shape templates with the Karcher mean. The seamless updating of abnormal events enhances the clustering performance for power systems fault detection. The algorithm is validated using the actual data from real-time hardware-in-the-loop testbed.