20 research outputs found
Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation
We study distributed average consensus problems in multi-agent systems with
directed communication links that are subject to quantized information flow.
The goal of distributed average consensus is for the nodes, each associated
with some initial value, to obtain the average (or some value close to the
average) of these initial values. In this paper, we present and analyze a
distributed averaging algorithm which operates exclusively with quantized
values (specifically, the information stored, processed and exchanged between
neighboring agents is subject to deterministic uniform quantization) and relies
on event-driven updates (e.g., to reduce energy consumption, communication
bandwidth, network congestion, and/or processor usage). We characterize the
properties of the proposed distributed averaging protocol on quantized values
and show that its execution, on any time-invariant and strongly connected
digraph, will allow all agents to reach, in finite time, a common consensus
value represented as the ratio of two integer that is equal to the exact
average. We conclude with examples that illustrate the operation, performance,
and potential advantages of the proposed algorithm
Education and training needs, methods, and tools
The importance of education and training in the domain of power and energy systems targeting the topics of cyber-physical energy systems/smart grids is discussed in this chapter. State-of-the art laboratory-based and simulation-based tools are presented, aiming to address the new educational needs
Developing a matlab tool while exploiting neural networks for combined prediction of hour's ahead system load along with irradiation, to estimate the system load covered by PV integrated systems
Privacy-Preserving Event-Triggered Quantized Average Consensus
In this paper, we propose a privacy-preserving event-triggered quantized average consensus algorithm that allows agents to calculate the average of their initial values without revealing to other agents their specific value. We assume that agents (nodes) interact with other agents via directed communication links (edges), forming a directed communication topology (digraph). The proposed distributed algorithm can be followed by any agent wishing to maintain its privacy (i.e., not reveal the initial value it contributes to the average) to other, possibly multiple, curious but not malicious agents. Curious agents try to identify the initial values of other agents, but do not interfere in the computation in any other way. We develop a distributed strategy that allows agents while processing and transmitting quantized information, to preserve the privacy of their initial quantized values and at the same time to obtain, after a finite number of steps, the exact average of the initial values of the nodes. Illustrative examples demonstrate the validity and performance of our proposed algorithm.Peer reviewe