ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact
With the widespread deployment of photovoltaic (PV) power stations, timely identification and rectification of module defects are crucial for extending service life and preserving efficiency. PV arrays, subjected to severe outside circumstances, are prone to defects exacerbated by dust accumulation,...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/4/1035 |
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| author | Xi Liu Hui Hwang Goh Haonan Xie Tingting He Weng Kean Yew Dongdong Zhang Wei Dai Tonni Agustiono Kurniawan |
| author_facet | Xi Liu Hui Hwang Goh Haonan Xie Tingting He Weng Kean Yew Dongdong Zhang Wei Dai Tonni Agustiono Kurniawan |
| author_sort | Xi Liu |
| collection | DOAJ |
| description | With the widespread deployment of photovoltaic (PV) power stations, timely identification and rectification of module defects are crucial for extending service life and preserving efficiency. PV arrays, subjected to severe outside circumstances, are prone to defects exacerbated by dust accumulation, potentially leading to complex compound faults. The resemblance between individual and compound faults sometimes leads to misclassification. To address this challenge, this paper presents a novel hybrid deep learning model, ResGRU, which integrates a residual network (ResNet) with bidirectional gated recurrent units (BiGRU) to improve fault diagnostic accuracy. Additionally, a Squeeze-and-Excitation (SE) module is incorporated to enhance relevant features while suppressing irrelevant ones, hence improving performance. To further optimize inter-class separability and intra-class compactness, a center loss function is employed as an auxiliary loss to enhance the model’s discriminative capacity. This proposed method facilitates the automated extraction of fault features from I-V curves and accurate diagnosis of individual faults, partial shading scenarios, and compound faults under varying levels of dust accumulation, hence aiding in the formulation of efficient cleaning schedules. Experimental findings indicate that the suggested model achieves 99.94% accuracy on pristine data and 98.21% accuracy on noisy data, markedly surpassing established techniques such as artificial neural networks (ANN), ResNet, random forests (RF), multi-scale SE-ResNet, and other ResNet-based approaches. Thus, the model offers a reliable solution for accurate PV array fault diagnosis. |
| format | Article |
| id | doaj-art-02f09b4996a84cf5871edee794b72d44 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-02f09b4996a84cf5871edee794b72d442025-08-20T02:01:24ZengMDPI AGSensors1424-82202025-02-01254103510.3390/s25041035ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust ImpactXi Liu0Hui Hwang Goh1Haonan Xie2Tingting He3Weng Kean Yew4Dongdong Zhang5Wei Dai6Tonni Agustiono Kurniawan7School of Electrical Engineering, Guangxi University, Nanning 530004, ChinaSchool of Engineering, Taylor’s University Lakeside Campus, Subang Jaya 47500, MalaysiaSchool of Electrical Engineering, Guangxi University, Nanning 530004, ChinaSchool of Electrical Engineering, Guangxi University, Nanning 530004, ChinaDepartment of Electrical Engineering, School of Engineering & Physical Sciences, Heriot-Watt University Malaysia Campus, Putrajaya 62200, MalaysiaSchool of Electrical Engineering, Guangxi University, Nanning 530004, ChinaSchool of Electrical Engineering, Guangxi University, Nanning 530004, ChinaCollege of the Environment and Ecology, Xiamen University, Xiamen 361102, ChinaWith the widespread deployment of photovoltaic (PV) power stations, timely identification and rectification of module defects are crucial for extending service life and preserving efficiency. PV arrays, subjected to severe outside circumstances, are prone to defects exacerbated by dust accumulation, potentially leading to complex compound faults. The resemblance between individual and compound faults sometimes leads to misclassification. To address this challenge, this paper presents a novel hybrid deep learning model, ResGRU, which integrates a residual network (ResNet) with bidirectional gated recurrent units (BiGRU) to improve fault diagnostic accuracy. Additionally, a Squeeze-and-Excitation (SE) module is incorporated to enhance relevant features while suppressing irrelevant ones, hence improving performance. To further optimize inter-class separability and intra-class compactness, a center loss function is employed as an auxiliary loss to enhance the model’s discriminative capacity. This proposed method facilitates the automated extraction of fault features from I-V curves and accurate diagnosis of individual faults, partial shading scenarios, and compound faults under varying levels of dust accumulation, hence aiding in the formulation of efficient cleaning schedules. Experimental findings indicate that the suggested model achieves 99.94% accuracy on pristine data and 98.21% accuracy on noisy data, markedly surpassing established techniques such as artificial neural networks (ANN), ResNet, random forests (RF), multi-scale SE-ResNet, and other ResNet-based approaches. Thus, the model offers a reliable solution for accurate PV array fault diagnosis.https://www.mdpi.com/1424-8220/25/4/1035photovoltaic (PV) systemfault diagnosisdust impactI-V curveresidual network (ResNet)bidirectional gated recurrent unit (BiGRU) |
| spellingShingle | Xi Liu Hui Hwang Goh Haonan Xie Tingting He Weng Kean Yew Dongdong Zhang Wei Dai Tonni Agustiono Kurniawan ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact Sensors photovoltaic (PV) system fault diagnosis dust impact I-V curve residual network (ResNet) bidirectional gated recurrent unit (BiGRU) |
| title | ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact |
| title_full | ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact |
| title_fullStr | ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact |
| title_full_unstemmed | ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact |
| title_short | ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact |
| title_sort | resgru a novel hybrid deep learning model for compound fault diagnosis in photovoltaic arrays considering dust impact |
| topic | photovoltaic (PV) system fault diagnosis dust impact I-V curve residual network (ResNet) bidirectional gated recurrent unit (BiGRU) |
| url | https://www.mdpi.com/1424-8220/25/4/1035 |
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