1. On Noise Resiliency of Neuromorphic Inferential Communication in Microgrids
- Author
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Song, Yubo, Sahoo, Subham, and Diao, Xiaoguang
- Subjects
Computer Science - Emerging Technologies ,Computer Science - Neural and Evolutionary Computing ,Electrical Engineering and Systems Science - Systems and Control - Abstract
Neuromorphic computing leveraging spiking neural network has emerged as a promising solution to tackle the security and reliability challenges with the conventional cyber-physical infrastructure of microgrids. Its event-driven paradigm facilitates promising prospect in resilient and energy-efficient coordination among power electronic converters. However, different from biological neurons that are focused in the literature, microgrids exhibit distinct architectures and features, implying potentially diverse adaptability in its capabilities to dismiss information transfer, which remains largely unrevealed. One of the biggest drawbacks in the information transfer theory is the impact of noise in the signaling accuracy. Hence, this article hereby explores the noise resiliency of neuromorphic inferential communication in microgrids through case studies and underlines potential challenges and solutions as extensions beyond the results, thus offering insights for its implementation in real-world scenarios., Comment: This manuscript has been accepted for publication in 2024 IEEE Energy Conversion Congress and Exposition (ECCE)
- Published
- 2024