Spike Talk: Genesis and Neural Coding Scheme Translations

TL;DR

Spike Talk proposes a neuromorphic-inspired, cyber-free framework for decentralized secondary control in microgrids by encoding power-flow-derived information as binary spikes and processing them with Leaky Integrate-and-Fire neurons at the DER level. It builds a detailed neuron-to-DER mapping (RC membranes, synapses as tie-lines) and uses rate, latency, and burst coding schemes to generate event-driven control signals, enabling on-device Hebbian-style plasticity to adapt dynamic droop (ΔR). The approach promises reduced cyber-layer dependencies, lower communication overhead, and energy-efficient, scalable coordination, demonstrated in a DC microgrid model with online learning and performance analyses across coding schemes. Limitations include partial theoretical guarantees and the need for extensive validation of stability, multiplexing of tie-lines, and real-world deployment constraints.

Abstract

Although digitalization of future power grids offer several coordination incentives, the reliability and security of information and communication technologies (ICT) hinders its overall performance. In this paper, we introduce a novel architecture Spike Talk via a unified representation of power and information as a means of data normalization using spikes for coordinated control of microgrids. This grid-edge technology allows each distributed energy resource (DER) to execute decentralized secondary control philosophy independently by interacting among each other using power flow along the tie-lines. Inspired from the field of computational neuroscience, Spike Talk basically builds on a fine-grained parallelism on the information transfer theory in our brains, particularly when neurons (modeled as DERs) transmit information (inferred from power streams measurable at each DER) through synapses (modeled as tie-lines). Not only does Spike Talk simplify and address the current bottlenecks of the cyber-physical architectural operation by dismissing the ICT layer, it provides intrinsic operational and cost-effective opportunities in terms of infrastructure development, computations and modeling. Hence, this paper provides a pedagogic illustration of the key concepts and design theories. Since we focus on coordinated control of microgrids in this paper, the signaling accuracy and system performance is studied for several neural coding schemes responsible for converting the real-valued local measurements into spikes.

Figures (11)

• Figure 1: As opposed to the current cyber-physical architecture in (a), an innovative co-transfer technology (b) Spike Talk emanates out of novel protocol, namely publish-subscribe, that supports the inferential exchange of information from power flows in the network. It is a software-defined methodology that offers cognitive abilities to DERs and their coordination by providing numerous benefits without requiring any infrastructural changes.
• Figure 2: Modeling analogies between neurons and DER: (a) neuron membrane and synpases, (b) the electrical modeling of the membrane as a $\mathrm{RC}$ circuit, which is retrofit into the output capacitor and equivalent tie-line resistance seen from a DC/DC converter, (c) Similar to the pre-synpatic and post-synaptic connection in (a), two DC DERs (emulated as neurons) are connected to each other via tie-lines (emulated as synpase) to transmit ingrained information via power, (d) structural mapping of each component in the brain and power grid to construct a duality principle.
• Figure 3: (a) Primary control of a DC DER with $\mathrm{R_d}$ and $\mathrm{\Delta R}$ as the static and adaptive droop values, respectively, (b) Single line diagram of the 4-bus DER based microgrid, (c) Spiking feedforward network with LIF neurons.
• Figure 4: LIF neuron model for generating spikes: (a) membrane potential for a synpatic current of 0.5 A only generates spikes with the lower voltage threshold $\mathrm{V_{th1}}$ = 0.4 V, (b) when the synaptic current is increased to 1 A, regularly interspaced spikes are generated for both the voltage thresholds.
• Figure 5: Neural coding schemes to represent dynamics in time-domain signals using: (a) rate coding, (b) latency coding, (c) burst coding. It is important to note that burst is a group of short inter-spaced interval (ISI) spikes.