@article{Etingov2026, 
author = {Dmitrii Etingov and Peng Zhang and Yacov A. Shamash},
title = {Neuromorphic traveling-wave protection for IoT-enabled autonomous microgrids},
year = {2026},
journal = {Cybernetics and Intelligence},
keywords = {Traveling wave protection, Spike Neural Networks, IoT, Microgrids, Hilbert Transform, Resilience},
url = {https://www.sciopen.com/article/10.26599/CAI.2026.9390013},
doi = {10.26599/CAI.2026.9390013},
abstract = {This paper explores the implementation of Traveling Wave Protection (TWP) in microgrids through the integration of Internet of Things (IoT) technologies and a Spiking Recurrent Neural Network (SRNN). Microgrids present unique fault de-tection challenges, as conventional protection techniques can be hindered by reduced fault currents, bidirectional power flow, and communication latency. By leveraging high-frequency traveling wave signals, TWP offers rapid and precise fault localization. In parallel, IoT-enabled sensing provides real-time data acquisition and decentralized decision-making. The proposed SRNN further enhances fault classification and location accuracy by combining spiking neuron dynamics with recurrent memory. Hardware-in-the-loop experiments on both simplified and complex microgrids demonstrate the method’s effectiveness in minimizing misclas-sification while maintaining low latency and reduced power consumption. This work extends our previous IoT-based TWP research by adopting a neuromorphic framework suitable for microgrid edge deployments, paving the way for more adaptive and robust protection solutions in modern distribution networks.}
}