Classifying power system disturbances with DFA and 1-NN: a robust machine learning approach

Abstract This paper presents an efficient and straightforward machine learning (ML) algorithm designed for the rapid detection of the most frequently occurring disturbances in microgrid (MG) systems. A laboratory-based MG was constructed, including components such as nonlinear loads, compensating devices, inductive loads, grid synchronization features, a battery charging circuit, switching relays, instrument transformers, and a signal acquisition system. Initially, valuable informative data were filtered from the acquired signals using an unsupervised k -means clustering technique, followed by feature extraction through detrended fluctuation analysis (DFA). The feature matrix was then reduced by retaining only the most important principal components (PC) before applying the 1-Nearest Neighbour (1-NN) supervised ML algorithm. Finally, the proposed method was compared to eight other benchmark ML algorithms based on class-wise accuracy. Besides, the performance was also examined in noisy environments.