Robust compressive features based power quality events classification with Analog–Digital Mixing Network (ADMN)

In this paper, an Analog–Digital Mixing Network (ADMN) is advanced for simultaneously collecting data and classifying the Power Quality (PQ) events. Based on recently developed Compressed Sampling (CS) theory, power signals are sampled via a new robust and semi-supervised compressive sampling scheme, and then the recorded data are directly used as features for the subsequent classification. Moreover, an Online Sequential Learning Algorithm (OSLA) is proposed to learn the training data one-by-one or chunk by chunk, and discard them as long as the training procedure is completed to keep the memory bounded in online learning. Consequently, ADMN can collect data streams and classify them sequentially, which provides a promising way to deal with the “big data”. Some experiments are taken on the classification of real PQ events, and the experimental results show the efficiency and superiority of our proposed method to its counterparts.