Details
| Original language | English |
|---|---|
| Title of host publication | 2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 202-207 |
| Number of pages | 6 |
| ISBN (electronic) | 9781728177175 |
| ISBN (print) | 978-1-7281-7716-8, 978-1-7281-7718-2 |
| Publication status | Published - 2021 |
| Event | 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021 - Virtual, Shiga, Japan Duration: 26 Feb 2021 → 28 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.
Keywords
- ANFIS, Degree of Polymerization, Grid Partition, paper insulation, Subtractive Clustering, transformer
ASJC Scopus subject areas
- Energy(all)
- Energy Engineering and Power Technology
- Engineering(all)
- Electrical and Electronic Engineering
- Mathematics(all)
- Control and Optimization
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2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 202-207 9383371.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Estimating the DP Value of the Paper Insulation of Oil-Filled Power Transformers Using an ANFIS Algorithm
AU - Rhamadhan, Firza Zulmi
AU - Kinkeldey, Tobias
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.
PY - 2021
Y1 - 2021
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.
KW - ANFIS
KW - Degree of Polymerization
KW - Grid Partition
KW - paper insulation
KW - Subtractive Clustering
KW - transformer
UR - http://www.scopus.com/inward/record.url?scp=85103985257&partnerID=8YFLogxK
U2 - 10.1109/CPEEE51686.2021.9383371
DO - 10.1109/CPEEE51686.2021.9383371
M3 - Conference contribution
AN - SCOPUS:85103985257
SN - 978-1-7281-7716-8
SN - 978-1-7281-7718-2
SP - 202
EP - 207
BT - 2021 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Power, Energy and Electrical Engineering, CPEEE 2021
Y2 - 26 February 2021 through 28 February 2021
ER -