Fusing data in adaptive agent control systems for electrical grids

This paper reports a Monte Carlo analysis of a novel multi-agent technique for network-centric control in a microgrid scenario. Agents monitor a feeder line shared by n microgrids and collectively assess changes in the direction and magnitude of power system dynamic stability. Every agent samples voltage variation every few milliseconds, classifies observations, and votes to express and communicate their individual inferences about the state of system stability. Monte Carlo simulations are used to investigate the probability that rules for representing and fusing information reliably produce error-resilient collective outcomes (ERCOs) in centralised and decentralised networks. ERCOs overcome communications or decision-making errors to provide a window of opportunity for adapting to correct emergent instability or to minimize harm. Voting systems provide a semantics and syntax for relating low-level data to high-level inferences about network situations. Our results provide a basis for optimising the design of communications infrastructure to control electrical grid stability.