Online Internal Fault Identification in Transformers Based on Direction and Location of Magnetizing Signature Patterns

The transformers are designed to transmit and distribute electrical power. Depending on the size of the requirement, replacement costs will be more if any fault occurs. Performing offline and invasive test also added to the replacement cost. Hence there is an increasing need to move from traditional schedule based maintenance programs to condition based maintenance. A large number of techniques are available for transformer health monitoring however a focused approach is required for diagnostics. Considering the long service life of a power transformer and prevalent use of human judgment, there is a need to structure a knowledge base around expert knowledge while continuing to create new diagnostic capabilities which can be plugged in. A novel method of online detection of the faults in the transformer is done by using the spectral signature of the flux emitted by the transformer during the running condition. In the flux based transformed fault identification region based fault can be detected at the earlier stage. From the experimental result it has concluded that flux based fault detection is efficient method for prefault detection when compared to the existing method. The flux based fault discrimation in transformer is more efficient.