Table1_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx

<p>Monitoring energy quality events is crucial for maintaining the stability and reliability of power grids. This paper presents a novel system integrating Discrete Wavelet Transform (DWT) and Extreme Learning Machine (ELM) for detecting and classifying power quality disturbances. The DWT perf...

Full description

Saved in:

Bibliographic Details
Main Author:	Reagan Jean Jacques Molu (19712164) (author)
Other Authors:	Wulfran Fendzi Mbasso (19712167) (author), Kenfack Tsobze Saatong (19712170) (author), Serge Raoul Dzonde Naoussi (19712173) (author), Mohammed Alruwaili (15176199) (author), Ali Elrashidi (19712176) (author), Waleed Nureldeen (19712179) (author)
Published:	2024
Subjects:	Nuclear Engineering Carbon Sequestration Science Automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels) Carbon Capture Engineering (excl. Sequestration) Non-automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels) Chemical Engineering not elsewhere classified Power and Energy Systems Engineering (excl. Renewable Power) Renewable Power and Energy Systems Engineering (excl. Solar Cells) Energy Generation, Conversion and Storage Engineering Nuclear Engineering (incl. Fuel Enrichment and Waste Processing and Storage) Chemical Sciences not elsewhere classified energy quality monitoring power quality disturbances discrete wavelet transform extreme learning machine FPGA implementation real-time processing
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Table1_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx

Similar Items