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Fault Diagnosis for Photovoltaic Systems Based on Sensors
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Fault Diagnosis for Photovoltaic Systems Based on Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 3788

Special Issue Editor

Prof. Dr. Eduardo Quiles

E-Mail Website
Guest Editor
Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: brain–computer interface (BCI); supervision of complex systems; fault detection and diagnosis; improvement in electrical distribution; reliability evaluation of distribution systems; microgrids and smartgrids; integration of renewable energies in distribution systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photovoltaic (PV) systems can experience substantial damage that affects constituent materials such as metals, crystals, encapsulating polymers, and, especially, PV cells. Consequently, the PV plants will decrease their performance in terms of power generation capacity. Solar panels are exposed to high degradation due to outdoor operation. Therefore, a good combination of online predictive diagnosis techniques is required to improve performance and avoid failures leading to the interruption of power generation.

This Special Issue will focus on PV fault detection and classification techniques based on sensors, covering topics that include, but are not limited to, the following:

  • Sensors and sensing strategies for fault detection and diagnosis of PV devices;
  • Sensors and sensing strategies for PV system voltages, currents, energy, power, and other electrically relevant quantities;
  • Sensors and sensing strategies for irradiance, temperature, and other weather-related quantities;
  • IoT–PV sensors and applications;
  • Smart PV sensors;
  • PV sensor development and analysis;
  • Advanced PV sensor characterization;
  • Embedded implementation of sensors, preprocessing techniques, computational-oriented strategies, edge computing;
  • Calibration, characterization, and testing procedures for PV-oriented sensors;
  • Visual and thermal inspection fault diagnosis methods;
  • Electrical-based fault diagnosis methods;
  • Machine learning and soft-computing techniques for data processing, aggregation, filtering, and forecasting in PV systems and applications.

Prof. Dr. Eduardo Quiles
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • PV modules
  • PV plants
  • smart PV sensors
  • IRT sensors
  • self-powering sensors
  • calibration PV sensors
  • IoT sensors
  • predictive fault diagnosis
  • fault detection and diagnosis methods
  • machine learning methods

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Published Papers (3 papers)

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Research

27 pages, 11172 KiB  
Article
ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact
by Xi Liu, Hui Hwang Goh, Haonan Xie, Tingting He, Weng Kean Yew, Dongdong Zhang, Wei Dai and Tonni Agustiono Kurniawan
Sensors 2025, 25(4), 1035; https://doi.org/10.3390/s25041035 - 9 Feb 2025
Viewed by 810
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Fault Diagnosis for Photovoltaic Systems Based on Sensors)
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16 pages, 4069 KiB  
Article
Photovoltaic Array Fault Diagnosis and Localization Method Based on Modulated Photocurrent and Machine Learning
by Yebo Tao, Tingting Yu and Jiayi Yang
Sensors 2025, 25(1), 136; https://doi.org/10.3390/s25010136 - 29 Dec 2024
Viewed by 817
Abstract
Photovoltaic arrays are exposed to outdoor conditions year-round, leading to degradation, cracks, open circuits, and other faults. Hence, the establishment of an effective fault diagnosis system for photovoltaic arrays is of paramount importance. However, existing fault diagnosis methods often trade off between high [...] Read more.
Photovoltaic arrays are exposed to outdoor conditions year-round, leading to degradation, cracks, open circuits, and other faults. Hence, the establishment of an effective fault diagnosis system for photovoltaic arrays is of paramount importance. However, existing fault diagnosis methods often trade off between high accuracy and localization. To address this concern, this paper proposes a fault identification and localization approach for photovoltaic arrays based on modulated photocurrent and machine learning. By irradiating different frequency-modulated light, this method separates photocurrent and directly measures the photoelectric conversion efficiency of each panel, achieving both high accuracy and localization. Through machine learning classification algorithms, the current amplitude and frequency of each photovoltaic panel are identified to achieve fault identification and localization. Compared to other methods, the strengths of this method lie in its ability to achieve high-speed and high-accuracy fault identification and localization by measuring only the short-circuit current. Additionally, the equipment cost is low. The feasibility of the proposed method is demonstrated through practical experimentation. It is determined that when utilizing a neural network algorithm, the fault identification speed meets measurement requirements (5800 obs/s), and the fault diagnosis accuracy is optimal (97.8%). Full article
(This article belongs to the Special Issue Fault Diagnosis for Photovoltaic Systems Based on Sensors)
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27 pages, 16364 KiB  
Article
Methodology for Calculating the Damaged Surface and Its Relationship with Power Loss in Photovoltaic Modules by Electroluminescence Inspection for Corrective Maintenance
by Nieves Saborido-Barba, Carmen García-López, José Antonio Clavijo-Blanco, Rafael Jiménez-Castañeda and Germán Álvarez-Tey
Sensors 2024, 24(5), 1479; https://doi.org/10.3390/s24051479 - 24 Feb 2024
Viewed by 1422
Abstract
Photovoltaic panels are exposed to various external factors that can cause damage, with the formation of cracks in the photovoltaic cells being one of the most recurrent issues affecting their production capacity. Electroluminescence (EL) tests are employed to detect these cracks. In this [...] Read more.
Photovoltaic panels are exposed to various external factors that can cause damage, with the formation of cracks in the photovoltaic cells being one of the most recurrent issues affecting their production capacity. Electroluminescence (EL) tests are employed to detect these cracks. In this study, a methodology developed according to the IEC TS 60904-13 standard is presented, allowing for the calculation of the percentage of type C cracks in a PV panel and subsequently estimating the associated power loss. To validate the methodology, it was applied to a polycrystalline silicon module subjected to incremental damage through multiple impacts on its rear surface. After each impact, electroluminescence images and I-V curves were obtained and used to verify power loss estimates. More accurate estimates were achieved by assessing cracks at the PV cell level rather than by substring or considering the entire module. In this context, cell-level analysis becomes indispensable, as the most damaged cell significantly influences the performance of the photovoltaic model. Subsequently, the developed methodology was applied to evaluate the conditions of four photovoltaic panels that had been in operation, exemplifying its application in maintenance tasks. The results assisted in decision making regarding whether to replace or continue using the panels. Full article
(This article belongs to the Special Issue Fault Diagnosis for Photovoltaic Systems Based on Sensors)
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