|
Abstract
|
This paper proposes an ensemble learning-based method to determine the type and location of faults in a smart
distribution network. The employed scheme is a stacking based method that consists of two main levels, each of
which includes one or more independent classifiers. In the structure of the proposed method for first level,
multiclass support vector machine, K-nearest neighbors and random forest models are exploited. Also, for the
next (final) level, which is also called meta-classifier, a random forest based model has been used, which itself is a
kind of ensemble learning method. In order to supply the data used to feed the model, the voltage and current
measurements in some buses and lines have been used. The proposed model is tested using the data related to all
different types and location of faults and operating conditions in the IEEE 123-bus network. Besides, the model is
implemented on another distribution test system to consider wider range and specs for fault parameters and also
to comprise with other methods applied to it; high impedance faults are also regarded in this part. The obtained
results show the appropriate accuracy of the proposed model and its better performance than other considered
methods.
|