Estimating the DP Value of the Paper Insulation of Oil-Filled Power Transformers Using an ANFIS Algorithm

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autoren

  • Firza Zulmi Rhamadhan
  • Tobias Kinkeldey
  • Peter Werle
  • Suwarno Suwarno

Externe Organisationen

  • Institut Teknologi Bandung (ITB)
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Titel des Sammelwerks2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten202-207
Seitenumfang6
ISBN (elektronisch)9781728177175
ISBN (Print)978-1-7281-7716-8, 978-1-7281-7718-2
PublikationsstatusVeröffentlicht - 2021
Veranstaltung11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021 - Virtual, Shiga, Japan
Dauer: 26 Feb. 202128 Feb. 2021

Abstract

The condition of the transformer insulation has an impact on the transformer's performance during the operation. The aging of the oil-impregnated cellulose insulation and the associated loss of mechanical strength are the important factors that limit the life of expectancy of a transformer. To determine the condition of the oil-impregnated cellulose insulation, the Degree of Polymerization (DP) parameter is commonly used. An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed to predict the DP Value by the chemical characteristics and dissolved gas parameters (acidity, interfacial tension, CO, CO2, breakdown voltage, and water content of the oil). This paper generates some algorithms which are based on the input space partitioning method to generate rules (grid partition or subtractive clustering) and data is normalized or not. The estimation result has been observed and evaluated to provide that the ANFIS algorithm is suitable to estimate insulation condition on field operating transformers.

ASJC Scopus Sachgebiete

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Estimating the DP Value of the Paper Insulation of Oil-Filled Power Transformers Using an ANFIS Algorithm. / Rhamadhan, Firza Zulmi; Kinkeldey, Tobias; Werle, Peter et al.
2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. S. 202-207 9383371.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Rhamadhan, FZ, Kinkeldey, T, Werle, P & Suwarno, S 2021, Estimating the DP Value of the Paper Insulation of Oil-Filled Power Transformers Using an ANFIS Algorithm. in 2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021., 9383371, Institute of Electrical and Electronics Engineers Inc., S. 202-207, 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021, Virtual, Shiga, Japan, 26 Feb. 2021. https://doi.org/10.1109/CPEEE51686.2021.9383371
Rhamadhan, F. Z., Kinkeldey, T., Werle, P., & Suwarno, S. (2021). Estimating the DP Value of the Paper Insulation of Oil-Filled Power Transformers Using an ANFIS Algorithm. In 2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021 (S. 202-207). Artikel 9383371 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CPEEE51686.2021.9383371
Rhamadhan FZ, Kinkeldey T, Werle P, Suwarno S. Estimating the DP Value of the Paper Insulation of Oil-Filled Power Transformers Using an ANFIS Algorithm. in 2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021. Institute of Electrical and Electronics Engineers Inc. 2021. S. 202-207. 9383371 doi: 10.1109/CPEEE51686.2021.9383371
Rhamadhan, Firza Zulmi ; Kinkeldey, Tobias ; Werle, Peter et al. / Estimating the DP Value of the Paper Insulation of Oil-Filled Power Transformers Using an ANFIS Algorithm. 2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. S. 202-207
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title = "Estimating the DP Value of the Paper Insulation of Oil-Filled Power Transformers Using an ANFIS Algorithm",
abstract = "The condition of the transformer insulation has an impact on the transformer's performance during the operation. The aging of the oil-impregnated cellulose insulation and the associated loss of mechanical strength are the important factors that limit the life of expectancy of a transformer. To determine the condition of the oil-impregnated cellulose insulation, the Degree of Polymerization (DP) parameter is commonly used. An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed to predict the DP Value by the chemical characteristics and dissolved gas parameters (acidity, interfacial tension, CO, CO2, breakdown voltage, and water content of the oil). This paper generates some algorithms which are based on the input space partitioning method to generate rules (grid partition or subtractive clustering) and data is normalized or not. The estimation result has been observed and evaluated to provide that the ANFIS algorithm is suitable to estimate insulation condition on field operating transformers.",
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AU - Werle, Peter

AU - Suwarno, Suwarno

N1 - Funding Information: ACKNOWLEDGMENT The authors would like to express their gratitude to GRIDINSPECT GmbH and AiF/ZiM for the financial support as well as Weidmann Electrical Technology AG for the support with insulation materials and Analysen Service GmbH Leipzig for the DP analysis.

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N2 - The condition of the transformer insulation has an impact on the transformer's performance during the operation. The aging of the oil-impregnated cellulose insulation and the associated loss of mechanical strength are the important factors that limit the life of expectancy of a transformer. To determine the condition of the oil-impregnated cellulose insulation, the Degree of Polymerization (DP) parameter is commonly used. An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed to predict the DP Value by the chemical characteristics and dissolved gas parameters (acidity, interfacial tension, CO, CO2, breakdown voltage, and water content of the oil). This paper generates some algorithms which are based on the input space partitioning method to generate rules (grid partition or subtractive clustering) and data is normalized or not. The estimation result has been observed and evaluated to provide that the ANFIS algorithm is suitable to estimate insulation condition on field operating transformers.

AB - The condition of the transformer insulation has an impact on the transformer's performance during the operation. The aging of the oil-impregnated cellulose insulation and the associated loss of mechanical strength are the important factors that limit the life of expectancy of a transformer. To determine the condition of the oil-impregnated cellulose insulation, the Degree of Polymerization (DP) parameter is commonly used. An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed to predict the DP Value by the chemical characteristics and dissolved gas parameters (acidity, interfacial tension, CO, CO2, breakdown voltage, and water content of the oil). This paper generates some algorithms which are based on the input space partitioning method to generate rules (grid partition or subtractive clustering) and data is normalized or not. The estimation result has been observed and evaluated to provide that the ANFIS algorithm is suitable to estimate insulation condition on field operating transformers.

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