Synapse-Inspired Energy Networks: A Neuromorphic Approach to Microgrid Protection without Communication Links
Saurabh Prabhakar, Bijaya Ketan Panigrahi, Frede Blaabjerg, Subham Sahoo

TL;DR
This paper presents a neuromorphic, communication-free protection system for microgrids that uses spike timing to detect faults rapidly and accurately, inspired by biological neural networks.
Contribution
It introduces a biologically inspired, synapse-like energy network that enables decentralized, real-time fault detection without communication links, improving speed and accuracy.
Findings
Fault detection latency of 10-58 ms, faster than traditional methods.
Detection accuracy exceeds 98%.
Spatial selectivity over 97%.
Abstract
Traditional protection systems for microgrids, which rely on high fault currents and continuous communication, struggle to keep up with the changing dynamics and cybersecurity concerns of decentralized networks. In this study, we introduce a novel biologically inspired protection system based on neuromorphic principles, where each distributed energy resource (DER) functions as a simple neuron. These neurons process local changes in voltage, current signals, and converting them into spike patterns that represent the severity of disturbances. Just as neurons communicate via synapses in biological systems, we exploit transmission cables to coordinate between DERs, enabling them to share information and respond to faults collectively. Fault detection and circuit breaker activation are driven by a First-To-Spike (FTTS) mechanism, similar to the concept of traveling wave protection, but…
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Taxonomy
TopicsMicrogrid Control and Optimization · Advanced Memory and Neural Computing · Power Systems Fault Detection
