Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques

<p dir="ltr">Partial shading in solar photovoltaic (PV) modules typically reduces the output current of the shaded PV module due to the reduction in the irradiance level. Although this phenomenon is temporary in nature, it is considered an intermittent fault, and it is essential for...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي: Kais Abdulmawjood (17947784) (author)
مؤلفون آخرون: Walid G. Morsi (22457716) (author)
منشور في: 2025
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author Kais Abdulmawjood (17947784)
author2 Walid G. Morsi (22457716)
author2_role author
author_facet Kais Abdulmawjood (17947784)
Walid G. Morsi (22457716)
author_role author
dc.creator.none.fl_str_mv Kais Abdulmawjood (17947784)
Walid G. Morsi (22457716)
dc.date.none.fl_str_mv 2025-04-04T06:00:00Z
dc.identifier.none.fl_str_mv 10.1109/access.2025.3552733
dc.relation.none.fl_str_mv https://figshare.com/articles/journal_contribution/Analyzing_Partial_Shading_in_PV_Systems_Using_Wavelet_Packet_Transform_and_Empirical_Mode_Decomposition_Techniques/30393226
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Engineering
Communications engineering
Electrical engineering
Electronics, sensors and digital hardware
Information and computing sciences
Machine learning
Partial shading
photovoltaic (PV) fault
machine learning
fault detection
localization
Circuit faults
Mathematical models
Principal component analysis
Fault diagnosis
Accuracy
Real-time systems
Integrated circuit modeling
Feature extraction
Analytical models
dc.title.none.fl_str_mv Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques
dc.type.none.fl_str_mv Text
Journal contribution
info:eu-repo/semantics/publishedVersion
text
contribution to journal
description <p dir="ltr">Partial shading in solar photovoltaic (PV) modules typically reduces the output current of the shaded PV module due to the reduction in the irradiance level. Although this phenomenon is temporary in nature, it is considered an intermittent fault, and it is essential for a protection system to differentiate it from other fault conditions to avoid unnecessary tripping. The main problem in identifying partial shading in a PV system is the difficulty of extracting its features under different shading conditions. To solve this difficulty, this article proposes a novel approach combining Wavelet Packet Transform (WPT) along with Empirical Mode Decomposition (EMD) to extract the features of PV panel output voltage and string current signals during partial shading conditions. In the first stage, the WPT is used to split the PV voltage and string currents into specific sub-band frequencies, and then EMD is used to decompose the selected frequency bands into a number of intrinsic mode functions (IMFs) and a residual. The generated IMF components are then fed into the Random Forest (RF) algorithm designed for shading detection and classification. This proposed hybrid technique provides a high-resolution representation of the array voltage and string currents without loss in the time-frequency resolution, aiding in the detection of partial shading and differentiation of its strength. The results indicate that the proposed approach achieved a detection accuracy of 98.43% and a classification accuracy of 97.6%.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3552733" target="_blank">https://dx.doi.org/10.1109/access.2025.3552733</a></p>
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network_acronym_str Manara2
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spelling Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition TechniquesKais Abdulmawjood (17947784)Walid G. Morsi (22457716)EngineeringCommunications engineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesMachine learningPartial shadingphotovoltaic (PV) faultmachine learningfault detectionlocalizationCircuit faultsMathematical modelsPrincipal component analysisFault diagnosisAccuracyReal-time systemsIntegrated circuit modelingFeature extractionAnalytical models<p dir="ltr">Partial shading in solar photovoltaic (PV) modules typically reduces the output current of the shaded PV module due to the reduction in the irradiance level. Although this phenomenon is temporary in nature, it is considered an intermittent fault, and it is essential for a protection system to differentiate it from other fault conditions to avoid unnecessary tripping. The main problem in identifying partial shading in a PV system is the difficulty of extracting its features under different shading conditions. To solve this difficulty, this article proposes a novel approach combining Wavelet Packet Transform (WPT) along with Empirical Mode Decomposition (EMD) to extract the features of PV panel output voltage and string current signals during partial shading conditions. In the first stage, the WPT is used to split the PV voltage and string currents into specific sub-band frequencies, and then EMD is used to decompose the selected frequency bands into a number of intrinsic mode functions (IMFs) and a residual. The generated IMF components are then fed into the Random Forest (RF) algorithm designed for shading detection and classification. This proposed hybrid technique provides a high-resolution representation of the array voltage and string currents without loss in the time-frequency resolution, aiding in the detection of partial shading and differentiation of its strength. The results indicate that the proposed approach achieved a detection accuracy of 98.43% and a classification accuracy of 97.6%.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3552733" target="_blank">https://dx.doi.org/10.1109/access.2025.3552733</a></p>2025-04-04T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3552733https://figshare.com/articles/journal_contribution/Analyzing_Partial_Shading_in_PV_Systems_Using_Wavelet_Packet_Transform_and_Empirical_Mode_Decomposition_Techniques/30393226CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303932262025-04-04T06:00:00Z
spellingShingle Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques
Kais Abdulmawjood (17947784)
Engineering
Communications engineering
Electrical engineering
Electronics, sensors and digital hardware
Information and computing sciences
Machine learning
Partial shading
photovoltaic (PV) fault
machine learning
fault detection
localization
Circuit faults
Mathematical models
Principal component analysis
Fault diagnosis
Accuracy
Real-time systems
Integrated circuit modeling
Feature extraction
Analytical models
status_str publishedVersion
title Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques
title_full Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques
title_fullStr Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques
title_full_unstemmed Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques
title_short Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques
title_sort Analyzing Partial Shading in PV Systems Using Wavelet Packet Transform and Empirical Mode Decomposition Techniques
topic Engineering
Communications engineering
Electrical engineering
Electronics, sensors and digital hardware
Information and computing sciences
Machine learning
Partial shading
photovoltaic (PV) fault
machine learning
fault detection
localization
Circuit faults
Mathematical models
Principal component analysis
Fault diagnosis
Accuracy
Real-time systems
Integrated circuit modeling
Feature extraction
Analytical models