Title: Fusing data in adaptive agent control systems for electrical grids

Authors: Arnold B. Urken

Addresses: Department of Civil Engineering and Engineering Mechanics, Udall Center for Public Policy, University of Arizona, Tucson, AZ 85721, USA

Abstract: 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 minimise 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.

Keywords: electrical grids; grid stability; data fusion; voting methods; stability control; time; adaptive control; agent-based control; microgrid; multi-agent systems; MAS: agent-based systems; feeder line monitoring; power systems; dynamic stability; voltage variation; Monte Carlo simulation; semantics; communications infrastructure; design optimisation; error-resilient collective outcomes; ERCOs.

DOI: 10.1504/IJCIS.2016.075870

International Journal of Critical Infrastructures, 2016 Vol.12 No.1/2, pp.53 - 81

Received: 09 Apr 2014
Accepted: 11 Apr 2014
Published online: 10 Apr 2016 *

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