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 detection 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 (HIL) experiments on both simplified and complex microgrids demonstrate the method’s effectiveness in minimizing misclassification 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.