Disturbance Monitoring and Distributed Fault Diagnosis
The research is focused on the development of diagnostic techniques for fault and disturbance monitoring both over complex distributed power networks and also at Energy Storage System (ESS) component level. Reliability is obviously a key requirement for modern systems and the development of distributed methods for fault diagnosis is an emergent important topic. The increased scale and complexity of the considered systems implies a consequent increase in risk. The construction of model-based distributed disturbance monitoring and event detection embedding the physical environment, constituted by a nonlinear-uncertain large-scale model of the power network, the sensor level, the diagnosis layer and the communication networks, has the potential of significantly improve the time-effectiveness of monitoring complex distributed networks. The main goals of the research activity are:
- Design of distributed state estimation algorithms. This is developed through a distributed state estimation methodology based on a suitable consensus mechanism exploiting a decentralized Pareto optimization methodology allowing to minimize at each step both bias and variance of the estimation error and to design locally suitable residual signals with known confidence levels and definable rate of false alarms.
- Distributed fault diagnosis. We are working at the development of a model-based distributed fault detection architecture. Distributed learning and approximation algorithms are derived to address the model uncertainty of dynamical cross-influence among local units and sections of the network. Novel Plug and Play architectures are designed to allow the monitoring of large-scale systems with dynamic structure, changing over the time. The choice of an optimal topology for the distributed monitoring architecture is investigated, in terms of minimization of communication and computation costs and maximization of detectability and isolability capabilities.
- Thomas Parisini
- Francesca Boem
- Marios M. Polycarpou - KIOS Research Center for Intelligent Systems and Networks, Dept. of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus, (email@example.com)
- Riccardo M. G. Ferrari - Danieli Automation S.p.A., Buttrio, Italy, (firstname.lastname@example.org)
- Stefano Riverso - Dipartimento di Ingegneria Industriale e dell’Informazione, Universita` degli Studi di Pavia, Pavia, Italy, (email@example.com)
- Giancarlo Ferrari-Trecate - Dipartimento di Ingegneria Industriale e dell’Informazione, Universita` degli Studi di Pavia, Pavia, Italy, (firstname.lastname@example.org)
- Carlo Fischione - ACCESS Linnaeus Center, KTH Royal Institute of Technology, Stockholm, Sweden, (email@example.com)
- Yuzhe Xu - ACCESS Linnaeus Center, KTH Royal Institute of Technology, Stockholm, Sweden, (firstname.lastname@example.org)