Abstract
This paper presents an efficient protection scheme based on Gabor transform (GT) and ensemble of k-nearest neighbor (EKNN) algorithm for fault classification/identification in Hybrid AC-HVDC network integrated with the wind turbine. At the relay point, the technique initiates with the acquisition of time domain current and voltage signals, followed by frequency domain processing. The raw voltage and current signal are fed to the GT-based feature extractor and the standard deviation (SD) of the Gabor feature is further used for training of the EKNN classifier/detector. Three different EKNN classifier modules have been developed to perform the protection tasks. The proposed method's effectiveness has been tested for a a wide range of fault scenarios with varying fault parameters. The validation results show that combining GT with KNN can effectively distinguish between defective and healthy line, thereby achieving excellent performance for fault detection and classification in both AC and HVDC systems.
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