Integrated recognition of distribution system conditions using waveform feature learning model

Abstract

A waveform holds recognizable feature patterns. To extract such features of various equipment disturbance conditions from the waveform, this research work presents a practical model to estimate distribution line (DL) conditions by means of a multi-label extreme learning machine. The motivation for the waveform learning is to develop device embedded models which are capable of detecting and classifying abnormal operations on the DLs. In waveform analysis, power quality (PQ) waveform modeling criteria are adopted for pattern classification. Typical disturbance waveforms are applied as class models, and the formula-generated waveform features are compared with field-acquired waveforms for disturbance classification. In particular, filtered symmetrical components of the modified varying window scale are applied for feature extraction. The proposed model interacts suitably with the parameter update method in classifying the waveforms in real network situations. On the other hand, conditional structures for distribution networks are discovered with respect to distribution network configurations and measurement device characteristics by the time-scaled class map of obtained measurement data from the field. The condition structure holds a potential for determining additional DL conditions by means of event considerations and improving its classification performance through the update mechanism of the learning machine.