Fault diagnosis system for power transformers

Abstract EN

This paper introduces an artificial neural network (ANN) based fault diagnosis system (FDS) for power transformers.

The system is designed to detect, localize and finally classify faults.

The proposed FDS consists of three hierarchical levels.

In the first level, a preprocessing procedure for input data is performed.

In the second level, an ANN is designed to detect the fault and localize its side.

In the third level, there are two sub-diagnosis systems.

Each system is dedicated to one side and consists of one ANN designed to classify the fault.

This ANN is also cascaded with four parallel ANN's utilized to identify the faulted phase.

The performance of FDS is evaluated using samples from local measurements (three-phase primary voltage and primary & secondary currents).

These samples were generated using the EMTP simulation of the High-Dam 15.75/500 kV transformer substation in the 500 kV Upper Egypt network.

Different fault types were simulated.

Fault location and incipience time were also considered.

Evaluation results proved that the performance of the proposed FDS is promising.