Multi-Source Data-Cooperated Neutral Low-Resistance Grounding Cable Grid Faulty Segment Identification

The multi-source data-driven faulted segment identification methodology has spurred research interest recently. Based on analyzing the post-fault physical features of the three-core cable earthed strap current, this study develops an approach to identify the faulty cable segment(s) of neutral low-resistance grounding distribution grids with distributed generators. To eliminate the potential uncertainty of recorded data, introducing Mixed Integer Linear Programming to construct the fault diagnosis model. The model employs traditional fault indications, including alarms from remote fault indicators and metering signals of zero-sequence current, to promote the validity of identifications. Under the causal logic constraint of events, employing the expected fault scenario with the minimum discrepancy to available data to determine the true faulty cable segment(s). Additionally, the model decision variables can be used as the reference for anomalous data determination. The effectiveness and robustness of the proposed methodology are validated by applying it to a simulation model based on a real-life three-core cable utility feeder.