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Hilbert Transform Based Approach for Detection and Classification of Power Quality Disturbances

Ravishankar S. Kankale, Dr. S. R. Paraskar, Dr. S. S. Jadhao

Abstract


This Power quality disturbances (PQDs) have a significant impact on the efficient operation of electrical systems. Detecting and classifying these disturbances accurately is crucial for ensuring a stable and reliable power supply. This research paper explores the use of the Hilbert envelope and Feed Forward Neural Network (FFNN) in detecting and classifying the PQDs. The Hilbert envelope, derived from the Hilbert Transform (HT), provides a valuable technique for analyzing non-stationary signals. The paper outlines the methodology, implementation process, and detailed results, showcasing the effectiveness of the Hilbert envelope technique in enhancing the reliability of power systems. In this study we have mainly considered sags, swells, and interruptions disturbances which are analyzed using the Hilbert envelope for feature extraction. The features extracted from Hilbert envelope are then fed to FFNN classifier. This paper outlines the methodology, implementation process, and detailed results, showcasing the effectiveness of utilizing the Hilbert envelope and FFNN in detection and classification of PQDs.


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References


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