Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging
The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimi...
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MDPI AG
2023-09-01
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| Online Access: | https://www.mdpi.com/1996-1073/16/18/6455 |
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| author | Ahmed Sayadi Djillali Mahi Issouf Fofana Lakhdar Bessissa Sid Ahmed Bessedik Oscar Henry Arroyo-Fernandez Jocelyn Jalbert |
| author_facet | Ahmed Sayadi Djillali Mahi Issouf Fofana Lakhdar Bessissa Sid Ahmed Bessedik Oscar Henry Arroyo-Fernandez Jocelyn Jalbert |
| author_sort | Ahmed Sayadi |
| collection | DOAJ |
| description | The aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization (<i>DP<sub>V</sub></i>) and tensile index (<i>Tidx</i>)) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the <i>DP<sub>V</sub></i> and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners. |
| first_indexed | 2024-03-10T22:50:36Z |
| format | Article |
| id | doaj.art-c192071eb4504edd8cf0f3f68c920014 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T22:50:36Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c192071eb4504edd8cf0f3f68c9200142023-11-19T10:25:20ZengMDPI AGEnergies1996-10732023-09-011618645510.3390/en16186455Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal AgingAhmed Sayadi0Djillali Mahi1Issouf Fofana2Lakhdar Bessissa3Sid Ahmed Bessedik4Oscar Henry Arroyo-Fernandez5Jocelyn Jalbert6Laboratory of Studies and Development of Semiconductor and Dielectric Materials, LeDMaScD, University Amar Telidji of Laghouat, BP 37G Route of Ghardaïa, Laghouat 03000, AlgeriaLaboratory of Studies and Development of Semiconductor and Dielectric Materials, LeDMaScD, University Amar Telidji of Laghouat, BP 37G Route of Ghardaïa, Laghouat 03000, AlgeriaResearch Chair on the Aging of Power Network Infrastructure (ViAHT), University of Québec, Saguenay, QC G7H 2B1, CanadaMaterials Science and Informatics Laboratory (MSIL), University Ziane Achour of Djelfa, BP 3117 Route of Moudjbara, Djelfa 17000, AlgeriaLaboratory for Analysis and Control of Energy Systems and Electrical Systems (LACOSERE), Laghouat University, Laghouat 03000, AlgeriaResearch Institute d’Hydro-Québec, 1800 Boulevard Lionel-Boulet, Varennes, QC J3X 1S1, CanadaResearch Institute d’Hydro-Québec, 1800 Boulevard Lionel-Boulet, Varennes, QC J3X 1S1, CanadaThe aim of this research is to predict the mechanical properties along with the behaviors of standard insulating paper used in power transformers under thermal aging. This is conducted by applying an artificial neural network (ANN) trained with a multiple regression model and a particle swarm optimization (MR-PSO) model. The aging of the paper insulation is monitored directly by the tensile strength and the degree of polymerization of the solid insulation and indirectly by chemical markers using 2-furfuraldehyde compound content in oil (2-FAL). A mathematical model is then developed to simulate the mechanical properties (degree of polymerization (<i>DP<sub>V</sub></i>) and tensile index (<i>Tidx</i>)) of the aged insulation paper. First, the datasets obtained from experimental results are used to create the MR model, and then the optimizer method PSO is used to optimize its coefficients in order to improve the MR model. Then, an ANN method is trained using the MR-PSO to create a nonlinear correlation between the <i>DP<sub>V</sub></i> and the time, temperature, and 2-FAL values. The acquired results are assessed and compared with the experimental data. The model presents almost the same behavior. In particular, it has the capability to accurately simulate the nonlinear property behavior of insulation under thermal aging with an acceptable margin of error. Since the life expectancy of power transformers is directly related to that of the insulating paper, the proposed model can be useful to maintenance planners.https://www.mdpi.com/1996-1073/16/18/6455modelingpredictionmechanical behaviorparticle swarm optimizationneural networkspower transformers |
| spellingShingle | Ahmed Sayadi Djillali Mahi Issouf Fofana Lakhdar Bessissa Sid Ahmed Bessedik Oscar Henry Arroyo-Fernandez Jocelyn Jalbert Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging Energies modeling prediction mechanical behavior particle swarm optimization neural networks power transformers |
| title | Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging |
| title_full | Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging |
| title_fullStr | Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging |
| title_full_unstemmed | Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging |
| title_short | Modeling and Predicting the Mechanical Behavior of Standard Insulating Kraft Paper Used in Power Transformers under Thermal Aging |
| title_sort | modeling and predicting the mechanical behavior of standard insulating kraft paper used in power transformers under thermal aging |
| topic | modeling prediction mechanical behavior particle swarm optimization neural networks power transformers |
| url | https://www.mdpi.com/1996-1073/16/18/6455 |
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