Neuromorphic traveling-wave protection for IoT-enabled autonomous microgrids

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.