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
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/19/7217 |
| _version_ | 1797479437980139520 |
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| author | Oussama Laayati Hicham El Hadraoui Adila El Magharaoui Nabil El-Bazi Mostafa Bouzi Ahmed Chebak Josep M. Guerrero |
| author_facet | Oussama Laayati Hicham El Hadraoui Adila El Magharaoui Nabil El-Bazi Mostafa Bouzi Ahmed Chebak Josep M. Guerrero |
| author_sort | Oussama Laayati |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:46:41Z |
| format | Article |
| id | doaj.art-420cc3af593c4c91be780539119f185c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:46:41Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-420cc3af593c4c91be780539119f185c2023-11-23T20:15:10ZengMDPI AGEnergies1996-10732022-10-011519721710.3390/en15197217An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management SystemsOussama Laayati0Hicham El Hadraoui1Adila El Magharaoui2Nabil El-Bazi3Mostafa Bouzi4Ahmed Chebak5Josep M. Guerrero6Computer Science, Mechanical, Electronics and Telecommunication Laboratory (LMIET), Faculty of Sciences and Techniques (FST), Hassan First University of Settat (UH1), Settat 26000, MoroccoGreen Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, MoroccoGreen Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, MoroccoGreen Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, MoroccoComputer Science, Mechanical, Electronics and Telecommunication Laboratory (LMIET), Faculty of Sciences and Techniques (FST), Hassan First University of Settat (UH1), Settat 26000, MoroccoGreen Tech Institute (GTI), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, MoroccoCenter for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, DenmarkAfter 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.https://www.mdpi.com/1996-1073/15/19/7217smart gridpower transformerenergy managementPHMmulti-agentmachine learning |
| spellingShingle | Oussama Laayati Hicham El Hadraoui Adila El Magharaoui Nabil El-Bazi Mostafa Bouzi Ahmed Chebak Josep M. Guerrero 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 smart grid power transformer energy management PHM multi-agent machine learning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | ai layered with multi agent systems architecture for prognostics health management of smart transformers a novel approach for smart grid ready energy management systems |
| topic | smart grid power transformer energy management PHM multi-agent machine learning |
| url | https://www.mdpi.com/1996-1073/15/19/7217 |
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