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In this article, a conceptual approach is presented for pre-fault detection regarding the waveform analytic aspects of distribution monitoring and measuring devices. Also included are event patterns specifically arranged by feature and classification methods. A waveform class patterning algorithm on time-series is applied experimentally to field waveforms that were obtained for several years. The waveforms are processed with consideration of waveform classification and event sequence processing because these detect fault-related phenomena. This approach demonstrates that conspicuous patterns in fault-related sequences can be discovered by the data-driven structure described in the paper by designing the event structure depending on the network configuration. The application is applied in a pattern-learning and pattern-detection process so that integrating both approaches provides a meaningful consolidation for detecting abnormal conditions on distribution lines. This event-based fault prevention is employed using actual acquisition data from a domestic-scale distribution system and a unique sequence model is constructed to determine normal and abnormal conditions. Event index manipulation analysis on different risk levels defines the pattern and its impact on monitoring results. The proposed model guides recognition of event patterns and waveforms that can be pre-emptively detected in advance of distribution line failures.

Event Sequence Model Application for Prioritization and Detection of Pre-Fault Waveforms on Power Distribution Lines