This paper proposes fuzzy system and artificial neural network approaches to identify the

deterioration of the winding insulation paper (WIP) in the power transformer using dissolved gas

analysis (DGA) method. Using the IEC/IEEE DGA criteria and the gas concentration values as

references the fuzzy diagnosis system and neural network are built. The proposed systems are

verified using practical data collected from Electricity Board. The fuzzy system is tested with

triangular, trapezoidal and Gaussian membership functions and its effectiveness is analyzed

through simulation in terms of accuracy in identifying the transformer faults. The proposed Back

propagation network is verified to overcome the drawbacks of conventional methods. The

proposed schemes are simulated and tested in the software environment. The simulation results

are presented.

This paper proposes fuzzy system and artificial neural network approaches to identify the

deterioration of the winding insulation paper (WIP) in the power transformer using dissolved gas

analysis (DGA) method. Using the IEC/IEEE DGA criteria and the gas concentration values as

references the fuzzy diagnosis system and neural network are built. The proposed systems are

verified using practical data collected from Electricity Board. The fuzzy system is tested with

triangular, trapezoidal and Gaussian membership functions and its effectiveness is analyzed

through simulation in terms of accuracy in identifying the transformer faults. The proposed Back

propagation network is verified to overcome the drawbacks of conventional methods. The

proposed schemes are simulated and tested in the software environment. The simulation results

are presented.

This paper proposes fuzzy system and artificial neural network approaches to identify the

deterioration of the winding insulation paper (WIP) in the power transformer using dissolved gas

analysis (DGA) method. Using the IEC/IEEE DGA criteria and the gas concentration values as

references the fuzzy diagnosis system and neural network are built. The proposed systems are

verified using practical data collected from Electricity Board. The fuzzy system is tested with

triangular, trapezoidal and Gaussian membership functions and its effectiveness is analyzed

through simulation in terms of accuracy in identifying the transformer faults. The proposed Back

propagation network is verified to overcome the drawbacks of conventional methods. The

proposed schemes are simulated and tested in the software environment. The simulation results

are presented.

This paper proposes fuzzy system and artificial neural network approaches to identify the

deterioration of the winding insulation paper (WIP) in the power transformer using dissolved gas

analysis (DGA) method. Using the IEC/IEEE DGA criteria and the gas concentration values as

references the fuzzy diagnosis system and neural network are built. The proposed systems are

verified using practical data collected from Electricity Board. The fuzzy system is tested with

triangular, trapezoidal and Gaussian membership functions and its effectiveness is analyzed

through simulation in terms of accuracy in identifying the transformer faults. The proposed Back

propagation network is verified to overcome the drawbacks of conventional methods. The

proposed schemes are simulated and tested in the software environment. The simulation results

are presented.