TY - GEN
T1 - Estimating transformer oil parameters using artificial neural networks
AU - Ghunem, Refat Atef
AU - El-Hag, Ayman H.
AU - Assaleh, Khaled
AU - Dhaheri, Fatima Al
PY - 2009
Y1 - 2009
N2 - In this paper the correlation between dielectric strength, the water content and oil CO2/CO ratio with insulation resistance in oil-filled power transformers is studied using artificial neural networks. This correlation allows and improves the condition assessment of transformer insulation using the Megger test. This is because dielectric strength, water content and CO2/CO ratio are important parameters for determining the deterioration state of the transformer insulation. The neural network model is built using tests' data for nineteen power transformers. The data collected is the high voltage, medium voltage, and low voltage to ground insulation resistance, oil breakdown voltage, water content and oil CO2/CO ratio. The results propose an efficient model with a breakdown voltage, water content, and oil CO2/CO ratio prediction rates of 95%, 82.8%, and 87.3% respectively.
AB - In this paper the correlation between dielectric strength, the water content and oil CO2/CO ratio with insulation resistance in oil-filled power transformers is studied using artificial neural networks. This correlation allows and improves the condition assessment of transformer insulation using the Megger test. This is because dielectric strength, water content and CO2/CO ratio are important parameters for determining the deterioration state of the transformer insulation. The neural network model is built using tests' data for nineteen power transformers. The data collected is the high voltage, medium voltage, and low voltage to ground insulation resistance, oil breakdown voltage, water content and oil CO2/CO ratio. The results propose an efficient model with a breakdown voltage, water content, and oil CO2/CO ratio prediction rates of 95%, 82.8%, and 87.3% respectively.
KW - Co2/co ratio
KW - Condition assessment
KW - Dielectric strength
KW - Neural networks
KW - Water content
UR - https://www.scopus.com/pages/publications/77950650908
M3 - Conference contribution
AN - SCOPUS:77950650908
SN - 9789948427155
T3 - 2009 International Conference on Electric Power and Energy Conversion Systems, EPECS 2009
BT - 2009 International Conference on Electric Power and Energy Conversion Systems, EPECS 2009
T2 - 2009 International Conference on Electric Power and Energy Conversion Systems, EPECS 2009
Y2 - 10 November 2009 through 12 November 2009
ER -