YOLOv3-MSSA based hot spot defect detection for photovoltaic power stations
With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technol...
Saved in:
| Published in: | Journal of Measurements in Engineering Vol. 12; no. 1; pp. 23 - 39 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Kaunas
JVE International Ltd
01.03.2024
|
| Subjects: | |
| ISSN: | 2335-2124, 2424-4635 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technology has become a crucial aspect. The study presents a defect detection model for PV power stations using the YOLOv3 (You Only Look Once v3) algorithm. The model incorporates coordinate attention module (CAM) and self-attention module (SAM) to improve feature extraction in low-resolution conditions. The Multi objective Sparrow is employed to achieve multiple objectives. It is very contributing in the detection of low-resolution features. It shows that the research method can reduce the loss value to 0.009 after 400 iterations of the loss curve test. The precision-recall (P-R) curve generated by the research method only starts to drop sharply when the Recall value reaches 0.96. The number of parameters generated by the research method is 3.46×10
6
. The detection accuracy of the research method reaches 98.86 % when there are five defective fault types. The results indicate that the proposed research method offers improved detection speed and higher accuracy in identifying hot spot defects in PV power stations. This technology provides valuable support for hot spot defect detection and presents new opportunities for the field. |
|---|---|
| AbstractList | With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technology has become a crucial aspect. The study presents a defect detection model for PV power stations using the YOLOv3 (You Only Look Once v3) algorithm. The model incorporates coordinate attention module (CAM) and self-attention module (SAM) to improve feature extraction in low-resolution conditions. The Multi objective Sparrow is employed to achieve multiple objectives. It is very contributing in the detection of low-resolution features. It shows that the research method can reduce the loss value to 0.009 after 400 iterations of the loss curve test. The precision-recall (P-R) curve generated by the research method only starts to drop sharply when the Recall value reaches 0.96. The number of parameters generated by the research method is 3.46×10
6
. The detection accuracy of the research method reaches 98.86 % when there are five defective fault types. The results indicate that the proposed research method offers improved detection speed and higher accuracy in identifying hot spot defects in PV power stations. This technology provides valuable support for hot spot defect detection and presents new opportunities for the field. With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technology has become a crucial aspect. The study presents a defect detection model for PV power stations using the YOLOv3 (You Only Look Once v3) algorithm. The model incorporates coordinate attention module (CAM) and self-attention module (SAM) to improve feature extraction in low-resolution conditions. The Multi objective Sparrow is employed to achieve multiple objectives. It is very contributing in the detection of low-resolution features. It shows that the research method can reduce the loss value to 0.009 after 400 iterations of the loss curve test. The precision-recall (P-R) curve generated by the research method only starts to drop sharply when the Recall value reaches 0.96. The number of parameters generated by the research method is 3.46×106. The detection accuracy of the research method reaches 98.86 % when there are five defective fault types. The results indicate that the proposed research method offers improved detection speed and higher accuracy in identifying hot spot defects in PV power stations. This technology provides valuable support for hot spot defect detection and presents new opportunities for the field. With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technology has become a crucial aspect. The study presents a defect detection model for PV power stations using the YOLOv3 (You Only Look Once v3) algorithm. The model incorporates coordinate attention module (CAM) and self-attention module (SAM) to improve feature extraction in low-resolution conditions. The Multi objective Sparrow is employed to achieve multiple objectives. It is very contributing in the detection of low-resolution features. It shows that the research method can reduce the loss value to 0.009 after 400 iterations of the loss curve test. The precision-recall (P-R) curve generated by the research method only starts to drop sharply when the Recall value reaches 0.96. The number of parameters generated by the research method is 3.46*[10.sup.6]. The detection accuracy of the research method reaches 98.86% when there are five defective fault types. The results indicate that the proposed research method offers improved detection speed and higher accuracy in identifying hot spot defects in PV power stations. This technology provides valuable support for hot spot defect detection and presents new opportunities for the field. Keywords: YOLOV3, multi objective sparrow search algorithm (MSSA), hot spot defects, photovoltaic power plants, deep belief networks (DBN). With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technology has become a crucial aspect. The study presents a defect detection model for PV power stations using the YOLOv3 (You Only Look Once v3) algorithm. The model incorporates coordinate attention module (CAM) and self-attention module (SAM) to improve feature extraction in low-resolution conditions. The Multi objective Sparrow is employed to achieve multiple objectives. It is very contributing in the detection of low-resolution features. It shows that the research method can reduce the loss value to 0.009 after 400 iterations of the loss curve test. The precision-recall (P-R) curve generated by the research method only starts to drop sharply when the Recall value reaches 0.96. The number of parameters generated by the research method is 3.46*[10.sup.6]. The detection accuracy of the research method reaches 98.86% when there are five defective fault types. The results indicate that the proposed research method offers improved detection speed and higher accuracy in identifying hot spot defects in PV power stations. This technology provides valuable support for hot spot defect detection and presents new opportunities for the field. |
| Audience | Academic |
| Author | Gu, Kaiming Chen, Yong |
| Author_xml | – sequence: 1 givenname: Kaiming surname: Gu fullname: Gu, Kaiming – sequence: 2 givenname: Yong surname: Chen fullname: Chen, Yong |
| BookMark | eNp1kUtLAzEUhYMo-Fy7HXDlYmqeM5NlEV9YKVhduAqZzI1G2smYpFX_val1oyCBnJB7vjzu2Ufbve8BoWOCR5QIKc5eFzCimLIRZZw0W2iPcspLXjGxndeMiZISynfRUYyuxZzXnEvZ7KHbp-lkumLl3Ww2LlodoStefCrikKcOLJi1pCzO94X1oRhy2a_8PGlnisG_Qyhi0utyPEQ7Vs8jHP3oAXq8vHg4vy4n06ub8_GkNKxhqZSiYSBbajsjaG2AG2gsgLSkagk3xErS0QrLpgbTQge64UI3UFsrMYMK2AE62Zw7BP-2hJjUq1-GPl-pGOa4qvLXaHaNNq5nPQfleutT0CaPDhbO5PZZl_fHtcSYCcZkBk5_AdmT4CM962WM6mZ2_9t7tvGa4GMMYNUQ3EKHT0Ww-k5E5UTUOhH1nUgmxB_CuE3f8rPc_F_uC-Y9kSI |
| CitedBy_id | crossref_primary_10_1049_cth2_12732 crossref_primary_10_3390_fishes10060273 |
| Cites_doi | 10.1016/j.solener.2021.07.037 10.3233/JIFS-200778 10.21608/mjeer.2021.146237 10.1007/s12652-019-01212-6 10.18280/ijdne.150504 10.1080/00224065.2021.1948372 10.1021/acsaem.1c03206 10.1109/JPHOTOV.2021.3059425 10.47852/bonviewJCCE2202319 10.1177/09544062211019774 10.1002/pip.3191 10.24425/mms.2022.138548 10.1504/IJCVR.2020.10027325 10.47852/bonviewAIA2202524 10.1108/IJPCC-05-2020-0046 10.47852/bonviewAIA2202303 10.1002/pip.3479 10.1109/JPHOTOV.2020.3045680 10.26689/pbes.v4i3.2203 10.17586/2226-1494-2020-20-3-418-424 10.47852/bonviewJCCE2202192 10.1016/j.ifacol.2020.12.1994 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2024 JVE International Ltd. Copyright JVE International Ltd. Mar 2024 |
| Copyright_xml | – notice: COPYRIGHT 2024 JVE International Ltd. – notice: Copyright JVE International Ltd. Mar 2024 |
| DBID | AAYXX CITATION ISR 7TB 7U5 8FD FR3 KR7 L7M |
| DOI | 10.21595/jme.2023.23418 |
| DatabaseName | CrossRef Science in Context Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts Technology Research Database Engineering Research Database Civil Engineering Abstracts Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Solid State and Superconductivity Abstracts Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts Advanced Technologies Database with Aerospace |
| DatabaseTitleList | CrossRef Civil Engineering Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2424-4635 |
| EndPage | 39 |
| ExternalDocumentID | A790035339 10_21595_jme_2023_23418 |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GroupedDBID | AAYXX ALMA_UNASSIGNED_HOLDINGS CITATION M~E ISR 7TB 7U5 8FD FR3 KR7 L7M |
| ID | FETCH-LOGICAL-c383t-9583e9b2fdc527ce4ce8fee9f16b14c1f91d260987ecbedea845a8e7ff903e6e3 |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001220546600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2335-2124 |
| IngestDate | Mon Jun 30 07:18:46 EDT 2025 Tue Apr 16 05:11:04 EDT 2024 Sat Sep 28 21:03:24 EDT 2024 Tue Nov 18 22:35:05 EST 2025 Sat Nov 29 03:51:06 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c383t-9583e9b2fdc527ce4ce8fee9f16b14c1f91d260987ecbedea845a8e7ff903e6e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://www.extrica.com/article/23418/pdf |
| PQID | 3040664992 |
| PQPubID | 2049544 |
| PageCount | 17 |
| ParticipantIDs | proquest_journals_3040664992 gale_infotracacademiconefile_A790035339 gale_incontextgauss_ISR_A790035339 crossref_primary_10_21595_jme_2023_23418 crossref_citationtrail_10_21595_jme_2023_23418 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-03-01 |
| PublicationDateYYYYMMDD | 2024-03-01 |
| PublicationDate_xml | – month: 03 year: 2024 text: 2024-03-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Kaunas |
| PublicationPlace_xml | – name: Kaunas |
| PublicationTitle | Journal of Measurements in Engineering |
| PublicationYear | 2024 |
| Publisher | JVE International Ltd |
| Publisher_xml | – name: JVE International Ltd |
| References | key-10.21595/jme.2023.23418-cit3 key-10.21595/jme.2023.23418-cit2 key-10.21595/jme.2023.23418-cit5 key-10.21595/jme.2023.23418-cit4 key-10.21595/jme.2023.23418-cit1 key-10.21595/jme.2023.23418-cit14 key-10.21595/jme.2023.23418-cit13 key-10.21595/jme.2023.23418-cit16 key-10.21595/jme.2023.23418-cit15 key-10.21595/jme.2023.23418-cit18 key-10.21595/jme.2023.23418-cit17 key-10.21595/jme.2023.23418-cit19 key-10.21595/jme.2023.23418-cit7 key-10.21595/jme.2023.23418-cit10 key-10.21595/jme.2023.23418-cit21 key-10.21595/jme.2023.23418-cit6 key-10.21595/jme.2023.23418-cit20 key-10.21595/jme.2023.23418-cit9 key-10.21595/jme.2023.23418-cit12 key-10.21595/jme.2023.23418-cit23 key-10.21595/jme.2023.23418-cit8 key-10.21595/jme.2023.23418-cit11 key-10.21595/jme.2023.23418-cit22 |
| References_xml | – ident: key-10.21595/jme.2023.23418-cit8 doi: 10.1016/j.solener.2021.07.037 – ident: key-10.21595/jme.2023.23418-cit22 doi: 10.3233/JIFS-200778 – ident: key-10.21595/jme.2023.23418-cit5 doi: 10.21608/mjeer.2021.146237 – ident: key-10.21595/jme.2023.23418-cit7 doi: 10.1007/s12652-019-01212-6 – ident: key-10.21595/jme.2023.23418-cit9 doi: 10.18280/ijdne.150504 – ident: key-10.21595/jme.2023.23418-cit15 doi: 10.1080/00224065.2021.1948372 – ident: key-10.21595/jme.2023.23418-cit1 doi: 10.1021/acsaem.1c03206 – ident: key-10.21595/jme.2023.23418-cit6 doi: 10.1109/JPHOTOV.2021.3059425 – ident: key-10.21595/jme.2023.23418-cit21 doi: 10.47852/bonviewJCCE2202319 – ident: key-10.21595/jme.2023.23418-cit4 doi: 10.1177/09544062211019774 – ident: key-10.21595/jme.2023.23418-cit17 doi: 10.1002/pip.3191 – ident: key-10.21595/jme.2023.23418-cit20 doi: 10.24425/mms.2022.138548 – ident: key-10.21595/jme.2023.23418-cit11 doi: 10.1504/IJCVR.2020.10027325 – ident: key-10.21595/jme.2023.23418-cit16 doi: 10.47852/bonviewAIA2202524 – ident: key-10.21595/jme.2023.23418-cit10 doi: 10.1108/IJPCC-05-2020-0046 – ident: key-10.21595/jme.2023.23418-cit19 doi: 10.47852/bonviewAIA2202303 – ident: key-10.21595/jme.2023.23418-cit3 doi: 10.1002/pip.3479 – ident: key-10.21595/jme.2023.23418-cit2 doi: 10.1109/JPHOTOV.2020.3045680 – ident: key-10.21595/jme.2023.23418-cit13 doi: 10.26689/pbes.v4i3.2203 – ident: key-10.21595/jme.2023.23418-cit14 doi: 10.17586/2226-1494-2020-20-3-418-424 – ident: key-10.21595/jme.2023.23418-cit18 doi: 10.47852/bonviewJCCE2202192 – ident: key-10.21595/jme.2023.23418-cit12 doi: 10.1016/j.ifacol.2020.12.1994 – ident: key-10.21595/jme.2023.23418-cit23 |
| SSID | ssib044744998 |
| Score | 2.2624192 |
| Snippet | With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However,... |
| SourceID | proquest gale crossref |
| SourceType | Aggregation Database Enrichment Source Index Database |
| StartPage | 23 |
| SubjectTerms | Algorithms Defects Electric power generation Electric power production Feature extraction Geospatial data Modules Object recognition Photovoltaic cells Power plants Recall Research methodology Solar power plants |
| Title | YOLOv3-MSSA based hot spot defect detection for photovoltaic power stations |
| URI | https://www.proquest.com/docview/3040664992 |
| Volume | 12 |
| WOSCitedRecordID | wos001220546600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2424-4635 dateEnd: 99991231 omitProxy: false ssIdentifier: ssib044744998 issn: 2335-2124 databaseCode: M~E dateStart: 20140101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtZ1Rb9MwEIAtGDzwgkCAKBvIQkggRRmN7cT24zQVgVg3RMe0PVmJ44whllRLOu2J387ZcZKGDWk88BJFrhNH9yV39vV8h9CbaUqBOo1CkiU0ZJRNwyyO81BrnaVMaJK7PAVHe3x_Xxwfyy--XGrtygnwshRXV3L5X1FDG8C2W2f_AXd_U2iAc4AOR8AOx1uBPznYO7ik4Xyx2AmsjcqD71UTwOq1CXLjUhXnpjG6jzFcws8VKKkmPdPB0hZNs_6FwY93feY6H_yKLpp2LadhH86zakM1bM2wvnHX7wQ5qXyb9zYQNoRbtZbgaPaHq3IIPgKVRSiNQzCGbKRfybX3yCtLepMOhzmItPkufpzbLKaEbhMwtGIwV91f9J8WX8eNbRZfbl2zMIeVd9E9wmNplfX816zTMYxxBos8V6iwe9g245Mb9_141NFk5WaT7eYhh4_QQ48B77TgH6M7pnyCPq9Bxw46BqrYQsctdNxDxwAdr0PHDjruoD9F3z7MDnc_hr5MRqipoE0oY0GNzEiR65hwbZg2ojBGFlGSRUxHhYxyWLVKwY3OTG5SweJUGF4UckpNYugztFFWpXmOcKop3IbkNIooKxKeTmnGZRIT0PIJ1WyCtjt5KO1zyNtSJj8VrCWdABUIUFkBKifACXrXX7Bs06f8vetrK2Blk5KUNurpNF3VtQLMaoA6QW99p6KCgXXqN5HA49s8ZqOeWx0o5b_OWlEwWUkC_MmL24y2iR4M38AW2mguVuYluq8vm7P64pV7r34DdluKew |
| linkProvider | ISSN International Centre |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=YOLOv3-MSSA+based+hot+spot+defect+detection+for+photovoltaic+power+stations&rft.jtitle=Journal+of+Measurements+in+Engineering&rft.au=Gu%2C+Kaiming&rft.au=Chen%2C+Yong&rft.date=2024-03-01&rft.pub=JVE+International+Ltd&rft.issn=2335-2124&rft.volume=12&rft.issue=1&rft.spage=23&rft_id=info:doi/10.21595%2Fjme.2023.23418&rft.externalDBID=ISR&rft.externalDocID=A790035339 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2335-2124&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2335-2124&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2335-2124&client=summon |