Bio-inspired Microgrid Management based on Brain's Sensorimotor Gating

TL;DR

The paper tackles the challenge of dynamic disturbance handling and protection coordination in microgrids by proposing a brain-inspired Sensorimotor Gating-based Neuro-Microgrid (SG-NMG) framework. It introduces Prepulse Inhibition (PPI) and Prepulse Facilitation (PPF) as dual gating mechanisms mapped onto the MG hierarchy: PPI for protective damping in primary/secondary control and PPF for adaptive amplification in tertiary control, augmented by analytics and ML modules. The authors provide a conceptual architecture, workflow, and paralell mapping between neural circuits and microgrid components, and discuss how RL can support a learning gating policy. Open challenges include mathematical modeling of gating, digital-twin validation, and cross-disciplinary collaboration, with future directions toward multi-level gating and digital-twin-enabled validation for self-healing grids. Overall, the SG-NMG framework aims to deliver self-protective, adaptive, and resilient microgrids by incorporating context-aware, gating-based control across hierarchical layers.

Abstract

Microgrids are emerging as key enablers of resilient, sustainable, and intelligent power systems, but they continue to face challenges in dynamic disturbance handling, protection coordination, and uncertainty. Recent efforts have explored Brain Emotional Learning (BEL) controllers as bio-inspired solutions for microgrid control. Building on this growing trajectory, this article introduces a new paradigm for Neuro-Microgrids, inspired by the brain's sensorimotor gating mechanisms, specifically the Prepulse Inhibition (PPI) and Prepulse Facilitation (PPF). Sensorimotor gating offers a biological model for selectively suppressing or amplifying responses depending on contextual relevance. By mapping these principles onto the hierarchical control architecture of microgrids, we propose a Sensorimotor Gating-Inspired Neuro-Microgrid (SG-NMG) framework. In this architecture, PPI-like control decisions correspond to protective damping in primary and secondary management of microgrids, whereas PPF-like decisions correspond to adaptive amplification of corrective control actions. The framework is presented through analytical workflow design, neuro-circuitry analogies, and integration with machine learning methods. Finally, open challenges and research directions are outlined, including the mathematical modeling of gating, digital twin validation, and cross-disciplinary collaboration between neuroscience and industrial power systems. The resulting paradigm highlights sensorimotor gating as a promising framework for designing self-protective, adaptive, and resilient microgrids.

Figures (5)

• Figure 1: Measuring the performance of Sensorimotor Gating via Startle Reflex. (a) Pulse-alone single-trial recording with startle reflex $A$; (b) PPI single-trial recording with startle reflex $B<A$ (Inhibition); (c) PPF single-trial recording with startle reflex $C>A$ (Facilitation).
• Figure 2: High-level Multi-Microgrid Architecture. PCC: Point of Common Coupling; IBS: Intelligent Bypass Switch
• Figure 3: General Neuro-circuitry and Workflow of Brain Emotional Learning Controllers.
• Figure 4: Simplified Neuro-circuitry and Workflow of Sensorimotor Gating Pathways. A prepulse sets a timing-dependent gating factor via cortical–basal ganglia control over the PnC startle gateway, producing inhibition (PPI) or facilitation (PPF) of the subsequent startle output. AMYG–PFC act as salience and inhibitory supervisors analogous to BEL models.
• Figure 5: Generalized Hierarchical SG-NMG Controller Architecture for Primary and Secondary Control. DG: Distributed Generator; DL: Distributed Load; PMU: Phasor Measurement Unit; SG-NMG: Sensorimotor Gating-based Neuro-Microgrid.