An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

Oussama Laayati (), Hicham El Hadraoui, Adila El Magharaoui, Nabil El-Bazi, Mostafa Bouzi, Ahmed Chebak and Josep M. Guerrero
Additional contact information
Oussama Laayati: Computer Science, Mechanical, Electronics and Telecommunication Laboratory (LMIET), Faculty of Sciences and Techniques (FST), Hassan First University of Settat (UH1), Settat 26000, Morocco
Hicham El Hadraoui: Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco
Adila El Magharaoui: Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco
Nabil El-Bazi: Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco
Mostafa Bouzi: Computer Science, Mechanical, Electronics and Telecommunication Laboratory (LMIET), Faculty of Sciences and Techniques (FST), Hassan First University of Settat (UH1), Settat 26000, Morocco
Ahmed Chebak: Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco
Josep M. Guerrero: Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark

Energies, 2022, vol. 15, issue 19, 1-28

Abstract: After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning.

Keywords: smart grid; power transformer; energy management; PHM; multi-agent; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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