39 research outputs found
Distributed Algorithms for State Estimation in a Low Voltage Distribution Network
Sintesi di due algoritmi distribuiti e scalabili per la stima dello stato di una rete elettrica a bassa tensione (smartgrid), utilizzando oso misure di tensione e corrente ai nodi della rete.
In particolare la prima tecnica si basa su una versione approssimata dell'algoritmo di Jacobi. La seconda su una formulazione scalabile della procedura ADMM (alternate direction multiplier metodo)ope
A Partition-Based Implementation of the Relaxed ADMM for Distributed Convex Optimization over Lossy Networks
In this paper we propose a distributed implementation of the relaxed
Alternating Direction Method of Multipliers algorithm (R-ADMM) for optimization
of a separable convex cost function, whose terms are stored by a set of
interacting agents, one for each agent. Specifically the local cost stored by
each node is in general a function of both the state of the node and the states
of its neighbors, a framework that we refer to as `partition-based'
optimization. This framework presents a great flexibility and can be adapted to
a large number of different applications. We show that the partition-based
R-ADMM algorithm we introduce is linked to the relaxed Peaceman-Rachford
Splitting (R-PRS) operator which, historically, has been introduced in the
literature to find the zeros of sum of functions. Interestingly, making use of
non expansive operator theory, the proposed algorithm is shown to be provably
robust against random packet losses that might occur in the communication
between neighboring nodes. Finally, the effectiveness of the proposed algorithm
is confirmed by a set of compelling numerical simulations run over random
geometric graphs subject to i.i.d. random packet losses.Comment: Full version of the paper to be presented at Conference on Decision
and Control (CDC) 201
Asynchronous Distributed Optimization over Lossy Networks via Relaxed ADMM: Stability and Linear Convergence
In this work we focus on the problem of minimizing the sum of convex cost
functions in a distributed fashion over a peer-to-peer network. In particular,
we are interested in the case in which communications between nodes are prone
to failures and the agents are not synchronized among themselves. We address
the problem proposing a modified version of the relaxed ADMM, which corresponds
to the Peaceman-Rachford splitting method applied to the dual. By exploiting
results from operator theory, we are able to prove the almost sure convergence
of the proposed algorithm under general assumptions on the distribution of
communication loss and node activation events. By further assuming the cost
functions to be strongly convex, we prove the linear convergence of the
algorithm in mean to a neighborhood of the optimal solution, and provide an
upper bound to the convergence rate. Finally, we present numerical results
testing the proposed method in different scenarios.Comment: To appear in IEEE Transactions on Automatic Contro
Multi-agents adaptive estimation and coverage control using Gaussian regression
We consider a scenario where the aim of a group of agents is to perform the
optimal coverage of a region according to a sensory function. In particular,
centroidal Voronoi partitions have to be computed. The difficulty of the task
is that the sensory function is unknown and has to be reconstructed on line
from noisy measurements. Hence, estimation and coverage needs to be performed
at the same time. We cast the problem in a Bayesian regression framework, where
the sensory function is seen as a Gaussian random field. Then, we design a set
of control inputs which try to well balance coverage and estimation, also
discussing convergence properties of the algorithm. Numerical experiments show
the effectivness of the new approach
Modeling Resilience of Collaborative AI Systems
A Collaborative Artificial Intelligence System (CAIS) performs actions in
collaboration with the human to achieve a common goal. CAISs can use a trained
AI model to control human-system interaction, or they can use human interaction
to dynamically learn from humans in an online fashion. In online learning with
human feedback, the AI model evolves by monitoring human interaction through
the system sensors in the learning state, and actuates the autonomous
components of the CAIS based on the learning in the operational state.
Therefore, any disruptive event affecting these sensors may affect the AI
model's ability to make accurate decisions and degrade the CAIS performance.
Consequently, it is of paramount importance for CAIS managers to be able to
automatically track the system performance to understand the resilience of the
CAIS upon such disruptive events. In this paper, we provide a new framework to
model CAIS performance when the system experiences a disruptive event. With our
framework, we introduce a model of performance evolution of CAIS. The model is
equipped with a set of measures that aim to support CAIS managers in the
decision process to achieve the required resilience of the system. We tested
our framework on a real-world case study of a robot collaborating online with
the human, when the system is experiencing a disruptive event. The case study
shows that our framework can be adopted in CAIS and integrated into the online
execution of the CAIS activities.Comment: This paper is accepted at the 3rd International Conference on AI
Engineering - Software Engineering for AI (CAIN 2024), Lisbon, Portuga
Smart Grid State Estimation with PMUs Time Synchronization Errors
We consider the problem of PMU-based state estimation combining information
coming from ubiquitous power demand time series and only a limited number of
PMUs. Conversely to recent literature in which synchrophasor devices are often
assumed perfectly synchronized with the Coordinated Universal Time (UTC), we
explicitly consider the presence of time-synchronization errors in the
measurements due to different non-ideal causes such as imperfect satellite
localization and internal clock inaccuracy. We propose a recursive Kalman-based
algorithm which allows for the explicit offline computation of the expected
performance and for the real-time compensation of possible frequency mismatches
among different PMUs. Based on the IEEE C37.118.1 standard on PMUs, we test the
proposed solution and compare it with alternative approaches on both synthetic
data from the IEEE 123 node standard distribution feeder and real-field data
from a small medium voltage distribution feeder located inside the EPFL campus
in Lausanne.Comment: 10 page, 7 figure