ASSESS THE RISK LEVEL OF POWER TRANSFORMER DUE SHORT-CIRCUIT FAULTS BASED ON ANFIS

Abstract

A power transformer is an electrical machine that converts electrical power at different voltage levels. Faults, occur in power transformers, inhibit electrical power distribution to the consumer. Protection, therefore, of the power transformers is essential in power systems reliability. The power system can be reliable if the protection devices work well when there is a fault. A hybrid intelligent technique, which is a combination of Artificial Neural Network (ANN) and Fuzzy known as Adaptive Neuro-Fuzzy Inference Systems (ANFIS), was used in this research. The objective of this paper is the simulation of differential relays as a protection device on power transformers using Matlab/Simulink. Performance of differential relays for power transformers protection is carried out with internal and external fault scenarios. The input data were classified into three different input for ANFIS such as internal and external 1, internal and external 2, internal, external 1, and external 2, respectively. The error results of ANFIS training for the type of fault internal and external 1 is 9.46*10^-7, and types of fault internal and external 2 is 1.09*10^-6 internal, external 1 and external 2 are 8.59*10^-7. The results obtained from the simulation were accurate and shows that the ANFIS technique is an efficient method that gives less error and a great value. Finally, the technique can minimize faults with power transformers. Finally, to prove this method can reduce faults in the power transformer, the assess of this model has been carried out through the RMSE that has been generated which is zero.