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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2024
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| _version_ | 1852026493690970112 |
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| author | Reagan Jean Jacques Molu (19712164) |
| author2 | Wulfran Fendzi Mbasso (19712167) Kenfack Tsobze Saatong (19712170) Serge Raoul Dzonde Naoussi (19712173) Mohammed Alruwaili (15176199) Ali Elrashidi (19712176) Waleed Nureldeen (19712179) |
| author2_role | author author author author author author |
| author_facet | Reagan Jean Jacques Molu (19712164) Wulfran Fendzi Mbasso (19712167) Kenfack Tsobze Saatong (19712170) Serge Raoul Dzonde Naoussi (19712173) Mohammed Alruwaili (15176199) Ali Elrashidi (19712176) Waleed Nureldeen (19712179) |
| author_role | author |
| dc.creator.none.fl_str_mv | Reagan Jean Jacques Molu (19712164) Wulfran Fendzi Mbasso (19712167) Kenfack Tsobze Saatong (19712170) Serge Raoul Dzonde Naoussi (19712173) Mohammed Alruwaili (15176199) Ali Elrashidi (19712176) Waleed Nureldeen (19712179) |
| dc.date.none.fl_str_mv | 2024-09-20T13:29:41Z |
| dc.identifier.none.fl_str_mv | 10.3389/fenrg.2024.1435704.s008 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Table4_Enhancing_power_quality_monitoring_with_discrete_wavelet_transform_and_extreme_learning_machine_a_dual-stage_pattern_recognition_approach_docx/27074755 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | Table4_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <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 performs multi-resolution analysis to decompose signals into time-frequency components, capturing various disturbances such as sags, swells, and harmonics. The ELM classifier, trained on these decomposed signals, achieves an impressive classification accuracy of 99.69%, significantly outperforming conventional methods like STFT with SVM (97.22%) and FFT with ANN (99.30%). The system was validated on a Xilinx Zynq-7000 SoC FPGA, demonstrating real-time processing capabilities with a latency of 1.5 milliseconds and a power consumption of 1.8 W. These findings highlight the effectiveness of the proposed method for real-time, accurate, and energy-efficient power quality monitoring.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_0a5a67b553ee8ca420aaedeea44ce8b4 |
| identifier_str_mv | 10.3389/fenrg.2024.1435704.s008 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27074755 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Table4_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docxReagan Jean Jacques Molu (19712164)Wulfran Fendzi Mbasso (19712167)Kenfack Tsobze Saatong (19712170)Serge Raoul Dzonde Naoussi (19712173)Mohammed Alruwaili (15176199)Ali Elrashidi (19712176)Waleed Nureldeen (19712179)Nuclear EngineeringCarbon Sequestration ScienceAutomotive 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 classifiedPower and Energy Systems Engineering (excl. Renewable Power)Renewable Power and Energy Systems Engineering (excl. Solar Cells)Energy Generation, Conversion and Storage EngineeringNuclear Engineering (incl. Fuel Enrichment and Waste Processing and Storage)Chemical Sciences not elsewhere classifiedenergy quality monitoringpower quality disturbancesdiscrete wavelet transformextreme learning machineFPGA implementationreal-time processing<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 performs multi-resolution analysis to decompose signals into time-frequency components, capturing various disturbances such as sags, swells, and harmonics. The ELM classifier, trained on these decomposed signals, achieves an impressive classification accuracy of 99.69%, significantly outperforming conventional methods like STFT with SVM (97.22%) and FFT with ANN (99.30%). The system was validated on a Xilinx Zynq-7000 SoC FPGA, demonstrating real-time processing capabilities with a latency of 1.5 milliseconds and a power consumption of 1.8 W. These findings highlight the effectiveness of the proposed method for real-time, accurate, and energy-efficient power quality monitoring.</p>2024-09-20T13:29:41ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fenrg.2024.1435704.s008https://figshare.com/articles/dataset/Table4_Enhancing_power_quality_monitoring_with_discrete_wavelet_transform_and_extreme_learning_machine_a_dual-stage_pattern_recognition_approach_docx/27074755CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270747552024-09-20T13:29:41Z |
| spellingShingle | Table4_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx Reagan Jean Jacques Molu (19712164) 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 |
| status_str | publishedVersion |
| title | Table4_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx |
| title_full | Table4_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx |
| title_fullStr | Table4_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx |
| title_full_unstemmed | Table4_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx |
| title_short | Table4_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx |
| title_sort | Table4_Enhancing power quality monitoring with discrete wavelet transform and extreme learning machine: a dual-stage pattern recognition approach.docx |
| topic | 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 |