In this paper, we infer ingrained remote information in AC power flows using spiking neural network (SNN) as edge processors for efficient coordination of power electronic converters. This work unifies power and information as a means of data normalization using a multi-modal regime in the form of spikes using energy-efficient neuromorphic processing and semantics theory. Firstly, we organize the synchronous realvalued measurements at each edge and translate them into asynchronous spike-based events to collect sparse data for training of SNN at each edge. Instead of relying on error-dependent supervised data-driven learning theory, we exploit the latency-driven unsupervised Hebbian learning rule to obtain modulation pulses for switching of power electronic converters that can now communicate among each other. Not only does this philosophy block exogenous path arrival for cyber attackers by dismissing the cyber layer, it also entails converter adaptation to system reconfiguration and parameter mismatch issues. We conclude this work by validating its energy-efficient and effective online learning performance under various scenarios in modified IEEE 14-bus system and under experimental conditions.

翻译：本文采用脉冲神经网络作为边缘处理器，以推断交流潮流中固有的远程信息，从而实现电力电子变换器的高效协调。本研究通过能量高效的神经形态处理与语义理论，以脉冲形式的多模态机制将功率与信息统一为数据归一化手段。首先，我们在每个边缘节点组织同步实值测量数据，并将其转换为基于脉冲的异步事件，从而收集稀疏数据用于各边缘SNN的训练。区别于依赖误差驱动的监督式数据驱动学习理论，我们采用基于延迟的无监督赫布学习规则来获取调制脉冲，用于控制可实现相互通信的电力电子变换器开关。该方法不仅通过摒弃网络层来阻断网络攻击者的外部路径入侵，还能使变换器适应系统重构与参数失配问题。最后，我们在改进的IEEE 14节点系统中通过多种场景及实验条件，验证了该方法在能量效率与在线学习性能方面的有效性。