University of Pannonia, Faculty of Information Technology
Department of Electrical Engineering and Information Systems

Energy project - Decision Support Systems sub-project

Project Identifier: Energy project (TAMOP 4.2.2.A-11/1/KONV - 2012-0072)
Project Name: Planning and optimization of modernization and efficient operation of energy supply and utilization systems using renewable energy sources and ICTs
Decision support systems sub-project
Duration: 01.11.2012 - 28.02.2015
Contact Person: Katalin M. Hangos, DSc, Professor

Tel: 06-88-624-607
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The Aim of the Sub-Project

Development and analysis of decision support systems for the operation, diagnosis and maintenance of energy-efficient energy-distribution systems with renewable energy sources and energy-sensitive consumers.

Results

System-wide maintenance scheduling. Semi-online cases of hierarchical scheduling problems (known total length of low-hierarchy jobs; known total length in both hierarchy levels; known total length of low-hierarchy jobs with a buffer available) were analyzed and optimal scheduling algorithms were developed thereon.
Diagnosis with colored Petri nets. Discrete event energy systems were modeled using timed colored Petri nets for fault detection and isolation. An efficient algorithm for constructing the occurrence graph of the model was developed. The diagnosis is based on traversing the occurrence graph that is equipped with probabilistic edge weights.

Cooperative scheduling algorithms. A generic multi-purpose scheduling algorithm has been developed. This is applied to route planning of accumulator bank servicing robots based on agent systems, where the communication protocols between the robots were also determined.

Dignosis based on risk assessment (HAZID) information. The diagnosis method based on blended HAZOP and FMEA (BL-HAZID) results has been extended to the time depended case, when event sequences are analyzed. A hierarchical decomposition method was suggested for the time-dependent BL-HAZID tables, and the diagnostic method was also extended to perform model-based diagnosis on the decomposed tables, too.