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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

Author

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  • 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
    Green Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, 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)

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.

Suggested Citation

  • Oussama Laayati & Hicham El Hadraoui & Adila El Magharaoui & Nabil El-Bazi & Mostafa Bouzi & Ahmed Chebak & Josep M. Guerrero, 2022. "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," Energies, MDPI, vol. 15(19), pages 1-28, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7217-:d:931096
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    References listed on IDEAS

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    1. Godina, Radu & Rodrigues, Eduardo M.G. & Matias, João C.O. & Catalão, João P.S., 2016. "Smart electric vehicle charging scheduler for overloading prevention of an industry client power distribution transformer," Applied Energy, Elsevier, vol. 178(C), pages 29-42.
    2. Umar, Abdullah & Kumar, Deepak & Ghose, Tirthadip, 2022. "Blockchain-based decentralized energy intra-trading with battery storage flexibility in a community microgrid system," Applied Energy, Elsevier, vol. 322(C).
    3. Muhammad Sharil Yahaya & Norhafiz Azis & Mohd Zainal Abidin Ab Kadir & Jasronita Jasni & Mohd Hendra Hairi & Mohd Aizam Talib, 2017. "Estimation of Transformers Health Index Based on the Markov Chain," Energies, MDPI, vol. 10(11), pages 1-11, November.
    4. Oussama Laayati & Hicham El Hadraoui & Nasr Guennoui & Mostafa Bouzi & Ahmed Chebak, 2022. "Smart Energy Management System: Design of a Smart Grid Test Bench for Educational Purposes," Energies, MDPI, vol. 15(7), pages 1-31, April.
    5. Hazlee Azil Illias & Xin Rui Chai & Ab Halim Abu Bakar & Hazlie Mokhlis, 2015. "Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    6. Issouf Fofana & Yazid Hadjadj, 2016. "Electrical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers," Energies, MDPI, vol. 9(9), pages 1-26, August.
    7. Adila El Maghraoui & Younes Ledmaoui & Oussama Laayati & Hicham El Hadraoui & Ahmed Chebak, 2022. "Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine," Energies, MDPI, vol. 15(13), pages 1-22, June.
    8. Leonori, Stefano & Martino, Alessio & Frattale Mascioli, Fabio Massimo & Rizzi, Antonello, 2020. "Microgrid Energy Management Systems Design by Computational Intelligence Techniques," Applied Energy, Elsevier, vol. 277(C).
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    Cited by:

    1. Nabil El Bazi & Mustapha Mabrouki & Oussama Laayati & Nada Ouhabi & Hicham El Hadraoui & Fatima-Ezzahra Hammouch & Ahmed Chebak, 2023. "Generic Multi-Layered Digital-Twin-Framework-Enabled Asset Lifecycle Management for the Sustainable Mining Industry," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    2. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.
    3. Sun, YongTeng & Ma, HongZhong, 2024. "Research progress on oil-immersed transformer mechanical condition identification based on vibration signals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).

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