Fault Detection and Isolation in Smart-Grid Networks of Intelligent Power Routers Modeled as Probabilistic Boolean Networks

Author:

Rivera Torres Pedro J.123ORCID,Gershenson García Carlos24ORCID,Kanaan Izquierdo Samir3ORCID

Affiliation:

1. Distributed Information and Automation Laboratory, Institute for Manufacturing, Department of Engineering, University of Cambridge, Cambridge CB3 0FS, UK

2. Complexity Sciences Center, National Autonomous University of México, Circuito Centro Cultural S/N, Ciudad Universitaria, Coyoacán 04510, Ciudad de México, Mexico

3. Bioinformatics and Biomedical Signals Laboratory, Biomedical Engineering Research Center, School of Industrial Engineering, Universitat Politècnica de Catalunya, H Building, 4th Floor, Av. Diagonal, 647, Barcelona 08028, Catalunya, Spain

4. Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA

Abstract

A self-organizing complex-network modeling method, probabilistic Boolean networks, is presented as a model-based diagnostic system for detecting and isolating different types of faults, failures, and modes of operation in which a network of intelligent power routers is deployed over a standard power test case: the Western System Coordinating Council 9 Bus System. Such a system allows designers and engineering professionals to make educated decisions pertaining to the design of smart-grid systems endowed with intelligent power routers. There is a recurrent necessity to design reliable and fault-tolerant smart power systems, maintaining adequate operation and adherence to performance specifications, while keeping costs at the minimum. This diagnostics system will help achieve such goals: better design through thorough analysis of the conditions that lead to a fault on a smart grid, proper detection of these faults, and isolation of the respective assets.

Funder

National Autonomous University of México’s Postdoctoral Fellowship

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

Reference13 articles.

1. Metabolic stability and epigenesis in randomly constructed genetic nets;S. A. Kauffman;Journal of Theoretical Biology,1969

2. Learning by probabilistic boolean networks;M. Dorigo

3. Probabilistic boolean network modeling of an industrial machine;P. J. Rivera Torres;Journal of Intelligent Manufacturing,2018

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