| Abstract: |
| This paper deals with two new methods, based on k-NN algorithm, for fault detection and classification in distance
protection. In these methods, by finding the distance between each sample and its fifth nearest neighbor in a predefault
window, the fault occurrence time and the faulty phases are determined. The maximum value of the distances
in case of detection and classification procedures is compared with pre-defined threshold values. The main advantages
of these methods are: simplicity, low calculation burden, acceptable accuracy, and speed. The performance of the
proposed scheme is tested on a typical system in MATLAB Simulink. Various possible fault types in different fault
resistances, fault inception angles, fault locations, short circuit levels, X/R ratios, source load angles are simulated.
In addition, the performance of similar six well-known classification techniques is compared with the proposed
classification method using plenty of simulation data. |
| Key words: Short circuit faults, Fault detection, Fault classification, K nearest neighbor algorithm |
| DOI：10.1186/s41601-017-0063-z |
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| Fund: |
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