Time Series Classification for Detecting Fault Location in a DC Microgrid

Abstract

In this paper, the potential for time series classifiers to identify faults and their location in a DC Microgrid is explored. Two different classification algorithms are considered. First, a minimally random convolutional kernel transformation (MINIROCKET) is applied on the time series fault data. The transformed data is used to train a regularized linear classifier with stochastic gradient descent (SDG). Second, a continuous wavelet transform (CWT) is applied on the fault data and a convolutional neural network (CNN) is trained to learn the characteristic patterns in the CWT coefficients of the transformed data. The data used for training and testing the models are acquired from multiple fault simulations on a 750 VDC Microgrid modeled in PSCAD/EMTDC. The results from both classification algorithms are presented and compared. For an accurate classification of the fault location, the MINIROCKET and SGD Classifier model needed signals/features from several measurement nodes in the system. The CWT and CNN based model accurately identified the fault location with signals from a single measurement node in the system. By performing a self-learning monitoring and decision making analysis, protection relays equipped with time series classification algorithms can quickly detect the location of faults and isolate them to improve the protection operations on DC Microgrids.