Power Quality Data Mining Using Hybrid Feature Extraction Technique
Power Quality Data Mining Using Hybrid Feature Extraction Technique
- New search for: Sivaramakrishnan, Vidhya
- New search for: Mahadevan, Balaji
- New search for: Vijayarajan, Kamaraj
- New search for: Chokkadi, Shreesha
- New search for: Bandyopadhyay, Rajib
- New search for: Sivaramakrishnan, Vidhya
- New search for: Mahadevan, Balaji
- New search for: Vijayarajan, Kamaraj
This paper proposes an approach for identifying the nature of power quality disturbances using hybrid feature extraction technique combining S transform and Hilbert transform. Using the combined features obtained, the classification is performed using Extreme Learning Machine (ELM). The effectiveness of the proposed approach is tested using wide spectrum of power quality disturbances. The comparison with existing methods indicates that the proposed hybrid signal processing approach for feature extraction results in improved classification accuracy. Sensitivity of the proposed approach is examined for signals with noise and the results are presented.
Document information
Power Quality Data Mining Using Hybrid Feature Extraction Technique
Lect. Notes Electrical Eng.
2023-03-12
12 pages
Article/Chapter (Book)
Electronic Resource
English
Power quality disturbance , <italic>S</italic> transform , Hilbert transform , Extreme learning machine Engineering , Control, Robotics, Mechatronics , Electronics and Microelectronics, Instrumentation , Signal, Image and Speech Processing , Power Electronics, Electrical Machines and Networks , Physics and Astronomy
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