Table4_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx

Table4_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...

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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
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