Structured approach to downsize data administration and improve fault recognition accuracy for large power systems

Abstract

This study presents a structured pattern-recognition framework for fault pattern recognition in large power systems. The main novelty of the study is in the efficient use of techniques to reduce data acquisition and administration requirements and limiting the computational overhead without sacrificing fault recognition accuracy and reliability. Optimal fault recorder placement based on the criterion of ‘network observability’ has been suggested for real-time monitoring of voltage and current at strategic network locations. An efficient feature-screening method has been adopted to select only the recorders whose data are assessed to be critical for fault recognition. ‘Wavelet’-based multi-resolution decomposition facilitated the extraction of important fault features from recorded waveforms and feature-screening was leveraged, once more, to eliminate irrelevant features to ensure that the data presented to the fault classifier is precise and feature-enriched. Random forest ensemble classifier was used for final class prediction and location of faults through voting of several individual learners to achieve high prediction accuracy. The proposed approach proved to be computationally efficient and consistent when tested on IEEE 118-bus system.