Identification and localization of non-zero resistance short circuit faults in distribution feeders based on the theory of wavelets and artificial intelligence

Abstract EN

This paper introduces a radial distribution feeder protection scheme based on certain features extraction from current signals measurement at the substation.

The features are captured using the discrete wavelet transform (DWT).

Two digital signals processing methods are used to introduce those features to the 1) fault detection 2) identification and 3) localization schemes; the first one is the energy method and the second one is the root mean square method.

For the purpose of fault type identification, two systems are tested and compared, a Fuzzy Inference System (FIS) and Artificial Neural Network (ANN).

Fault location scheme is then built based on ANNs.

An effort is made to reduce the computational burden and the speed of detection provided by the fault detection and identification schemes.

Since the short circuit faults are the most likely types of faults that can occur in power systems, the ten types of these faults taking into account different fault resistances are simulated in MATLAB environment and the protection scheme is built based on the idea of over current.

The power quality disturbances such as switching transient events on the feeder is also taken into account in order to build a reliable and secure protection scheme.