AI Summary & Key Takeaways
AI InsightIt is shown that the technique presented correctly recognizes and discriminates the fault type and faulted phases(s) with a high degree of accuracy for different location and time of occurrence in the simulated model distribution system.
📌 3 Key Takeaways
Abstract
This paper presents an integrated design of a fault classifier for distribution systems using a hybrid Wavelet-artificial neural network (ANN) based approach. Data for the fault classifier is produced by PSCAD/EMTDC simulation program for 34.5 kV Sagmalcilar-Maltepe distribution system in Istanbul, Turkey. It is aimed to design a classifier capable of recognizing ten classes of three-phase distribution system faults. A database of line currents and line-to-ground voltages is built up including system faults at different fault inception angles and fault locations,. The characteristic information over six-channel of current and voltage samples is extracted by the wavelet multi-resolution analysis technique. Afterwards, an ANN-based tool was employed for classification task. The main idea in this approach is solving the complex fault (three-phase short-circuit) classification problem under various system and fault conditions. A self-organizing map, with Kohonen's learning algorithm and type-one learning vector quantization technique is implemented into the fault classification study. The performance of the wavelet-neural fault classifier is presented and the results are analyzed in the paper. It is shown that the technique presented correctly recognizes and discriminates the fault type and faulted phases(s) with a high degree of accuracy for different location and time of occurrence in the simulated model distribution system.