2021 IEEE International Conference on Cyber Security and Resilience

Full Program

Summary:

Several algorithms combining qualitative and quantitative components are currently used for splitting a large interconnected power grid into islands as a measure to provide the best reconfiguration option when a fault appears. The aim of this article is to compare the clustering results of a binary genetic algorithm and a deep learning based method in order to identify the differences and to find in which cases it is rather better applicable each of the techniques

Author(s):

Pol Paradell    
Institute for Energy Research of Catalonia (IREC)
Spain

Pol Paradell a technical specialist in power electronics, control systems, microcontrollers and programming in Python and C++. He has worked in EBE Engineering and associates, Electrical Engineering dedicated to the water sector, as electrical and control engineer, involved in the design of electrical installations, design of control systems for water pumping stations, assistant of work directly and in the accomplishment of projects of legalization of medium and low voltage, from 2007 until 2012. He has also worked in the Manresa Technology Center (CTM), a research center that collaborates with the MCIA group of the UPC as a grant holder, developing the programming of a spectrometer prototype, carrying out the programming in microcontrollers and the design and manufacture of a signal acquisition plate, collaboration carried out at the beginning of 2016. He has also worked for IREC, Catalonia's energy research centre, as a project engineer in the development of projects related to the electricity market, cybersecurity, electric vehicle charging systems and micro-network management, from 2016 to the present day.

Yannis Spyridis    
0Infinity Ltd
United Kingdom

Alba Colet    
Institute for Energy Research of Catalonia (IREC)
Spain

Industrial computing engineer specialized in the development of applications using Object-oriented programming in Microsoft .NET, C, C ++, Visual Basic and Visual C ++. In the industrial environment, interested in the implementation of SCADA, manufacturing executions systems and energy management applications, the integration of communication protocols and the development of proprietary drivers. Remarkable, experience in renewable energy and microgrids. Currently is working in the Catalonia Institute for Energy Research as a Laboratory leader for Energy smart lab, that comprises the Energy Analytics and Power electronics Area.

Anzhelika Ivanova    
Institute for Energy Research of Catalonia (IREC)
Spain

MSc. Eng. Anzhelika Ivanova received her BSc. degree in electrical engineering and computer science, specialized in electric power systems at the Faculty of Electrical Engineering and Computer Science, Ss. Cyril and Methodius University (UKIM), Skopje, Republic of Macedonia, in 2014. In 2016, she received a double M.S degree in electrical engineering and energy engineering form Technical University of Eindhoven (TUE), Eindhoven, Netherlands and Polytechnic University of Catalonia (UPC), Barcelona, Catalonia, respectively, through the InnoEnergy Master program Smart Electrical Networks and Systems. She developed her Master thesis in IREC, focusing on frequency support markets with wind power participation. In 2017 she joined IREC as a PhD student and project engineer. Her work includes integration of renewable power sources in electricity markets, optimization and state estimation in power networks and unbalanced distribution network analysis.

Jose Luis Dominguez - Garcia    
Institute for Energy Research of Catalonia (IREC)
Spain

Achilleas Sesis    
0Infinity Ltd
United Kingdom

Georgios Efstathopoulos    
0Infinity Ltd
United Kingdom

 


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