TY - GEN
T1 - Insulation Life Loss Prediction of an Oil-Filled Power Transformer Using Adaptive Neuro-Fuzzy Inference System
AU - Matsila, Hulisani
AU - Bokoro, Pitshou N.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this work, the performance accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in short-term prediction of insulation life loss is evaluated. A 50 Hz, Dyn11, 1000 kVA 11/0.4 kV oil-filled indoor power transformer, feeding an essential facility with mostly nonlinear and seasonally changing loads, is used. The 1735 Fluke power logger unit and the Fluke 59 mini-infrared thermometer are respectively used for total load current and ambient temperature recordings. The ANFIS, such as implemented in MatLab R2019b software package, is invoked to perform 24-hour computation and subsequently predict the status of insulation life for 7 consecutive days based on 24-hour measurement of the load current, ambient temperature and the hottest-spot temperature. Results show a MAPE of 6.51% for this technique in short-term prediction of insulation life loss of an oil-filled power transformer.
AB - In this work, the performance accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in short-term prediction of insulation life loss is evaluated. A 50 Hz, Dyn11, 1000 kVA 11/0.4 kV oil-filled indoor power transformer, feeding an essential facility with mostly nonlinear and seasonally changing loads, is used. The 1735 Fluke power logger unit and the Fluke 59 mini-infrared thermometer are respectively used for total load current and ambient temperature recordings. The ANFIS, such as implemented in MatLab R2019b software package, is invoked to perform 24-hour computation and subsequently predict the status of insulation life for 7 consecutive days based on 24-hour measurement of the load current, ambient temperature and the hottest-spot temperature. Results show a MAPE of 6.51% for this technique in short-term prediction of insulation life loss of an oil-filled power transformer.
KW - adaptive neuro- fuzzy inference system
KW - IEEE and IEC guidelines
KW - insulation life loss
KW - Oil-filled transformer
UR - http://www.scopus.com/inward/record.url?scp=85135820061&partnerID=8YFLogxK
U2 - 10.1109/ISIE51582.2022.9831734
DO - 10.1109/ISIE51582.2022.9831734
M3 - Conference contribution
AN - SCOPUS:85135820061
T3 - IEEE International Symposium on Industrial Electronics
SP - 792
EP - 798
BT - 2022 IEEE 31st International Symposium on Industrial Electronics, ISIE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st IEEE International Symposium on Industrial Electronics, ISIE 2022
Y2 - 1 June 2022 through 3 June 2022
ER -