Mechanism–Data Dual-Driven Outage Identification Framework for Distribution Networks with High Penetration of Inverter-Based Distributed Generation
DOI:
https://doi.org/10.4108/ew.14541Keywords:
Active distribution network, distributed generation, outage identification, Dempster–Shafer evidence theory, graph neural networkAbstract
INTRODUCTION: High penetration of inverter-based distributed generators (IBDGs) changes power-flow patterns and fault transients in distribution networks, reducing the effectiveness of conventional outage identification. Under the adopted low-voltage ride-through control model, IBDG fault currents are limited to 1.2–1.5 times the rated current, weakening protection discrimination and degrading classifiers trained on conventional fault features.
OBJECTIVES: To address the aforementioned challenges, this paper proposes a mechanism–data dual-driven outage identification framework for distribution networks with high levels of renewable energy penetration.
METHODS: The framework integrates improved weighted Dempster–Shafer (D–S) evidence theory for multi-source fusion, constructs an IBDG-aware fault-feature library covering converter-specific transient behaviors, and designs decision rules linking post-fault analysis with early fault warning.
RESULTS: Under the tested feeder, DG operating schemes, fault cases, and measurement-noise conditions, the proposed method achieved an F1-score of 96.8%. Its measured model-inference-and-fusion latency was 38 ms, compared with 120 ms for the reference implementation.
CONCLUSION: Under the tested feeder, operating schemes, fault cases, and measurement-noise conditions, the proposed method also achieved higher outage-identification performance than the implemented comparison methods.
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Copyright (c) 2026 Ning Liu, Xiaopeng Zhang, Xudong Wang, Guodong Li
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