Article Details

Abstract

To address the issues of false trips, failure to trip, and reclosing failures in traditional Feeder Automation (FA) systems caused by high proportions of Distributed Generation (DG), this study proposes an adaptive enhancement technology based on an edge collaborative computing architecture. The core technological contributions of this architecture include: an edge gateway collaborative computing framework deployed at distribution terminals for distributed data preprocessing and decision-making; a 5G low-latency communication mechanism ensuring millisecond-level information exchange between terminals; and an FTU autonomous decision-making algorithm based on distributed consensus algorithms, enabling multiple feeder terminal units to reach consensus on fault direction without central station intervention. Building upon these components, a three-tier mechanism is established for transient fault direction discrimination, dynamic setting adjustment, and adaptive reclosing. A 10 kV active distribution network model was implemented on the Real-Time Distribution System (RTDS), with DG penetration rates ranging from 50% to 200% and various fault scenarios simulated. Results demonstrate that at a DG penetration rate of 200%, the fault identification rate reached 96.2%, reclosing success rate 94.6%, false trip rate 2.0%, and average isolation time 0.41 seconds. Under high-resistance grounding conditions (500 Ω), asynchronous reclosures decreased from 18 to 2 instances, while impulse current dropped from 5.8 p.u. to 1.3 p.u. The proposed edge collaborative computing architecture significantly enhances FA adaptability under high DG deployment scenarios.

Keywords

high proportion of distributed power sources; feeder automation; transient direction discrimination; dynamic setting; adaptive reclosing

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About the authors

Zhou Minjie (1975-), male, Han nationality, from Mount Huangshan, Anhui Province, intermediate engineer, bachelor's degree, research direction: power electronic information.