Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach

Murillo-Soto, Luis Diego; Meza, Carlos

doi:10.3390/en14092397

Cited by 6 publications

(5 citation statements)

References 50 publications

(67 reference statements)

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“…It should be noticed that V max is the higher voltage in the string, and it belongs to the module with less degradation. This criterion is simple to incorporate into other fault detection proposals that use differential voltages as input signals such as [44,45].…”

Section: Discussionmentioning

confidence: 99%

Detection Criterion for Progressive Faults in Photovoltaic Modules Based on Differential Voltage Measurements

Murillo-Soto

Meza

2022

Applied Sciences

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PV modules may experience degradation conditions that affect their power efficiency and affect the rest of the PV array. Based on the literature review, this paper links the parameter variation on a PV module with the six most common degradation faults, namely, series resistance degradation, optical homogeneous degradation, optical heterogeneous degradation, potential induced degradation, micro-cracks, and light-induced degradation. A Monte Carlo-based numerical simulation was used to study the effect of the faults mentioned above in the voltage of the modules in a PV array with one faulty module. A simple expression to identify faults was derived based on the obtained results. The simplicity of this expression allows integrating the fault detection technique in low-cost electronic circuits embedded in a PV module, optimizer, or microinverter.

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Section: Discussionmentioning

confidence: 99%

Detection Criterion for Progressive Faults in Photovoltaic Modules Based on Differential Voltage Measurements

Murillo-Soto

Meza

2022

Applied Sciences

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“…In the first published paper [31], L.D. Murillo-Soto and C. Meza have proposed an automated fault management system based on a reconfiguration approach to managing two faults (i.e., short-circuit to ground and the open wires in the string) in any position inside the PV arrays.…”

Section: The Contribution Of the Special Issuementioning

confidence: 99%

Autonomous Monitoring and Analysis of Photovoltaic Systems

Aghaei

2022

Energies

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“…However, optical remote sensing monitoring can only be applied to the statistics and land cover planning of the normal operation of photovoltaic arrays, as well as the identification of faults or hidden dangers caused by external objects such as excessive surface area ash, bird manure attachment or branch cover in photovoltaic array blocks. However, due to the fact that the photovoltaic array with internal faults (short circuit, partial aging, crack) is not obvious in the optical remote sensing image (Murillo-Soto and Meza, 2021), it is impossible to detect and troubleshoot this part of the fault.…”

Section: Introductionmentioning

confidence: 99%

Study on Fault Monitoring Technology of Photovoltaic Panel Based on Thermal Infrared and Optical Remote Sensing

Zhang,

Wang,

Yao

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Rapid access to the operating status of Photovoltaic (PV) panels and troubleshooting can save management and maintenance costs for the development of PV power plants, which is important for PV power plant management and power generation capacity assurance. The use of remote sensing technology to identify the faults of photovoltaic panels has developed rapidly, however, current research usually relies only on a single optical data source to identify and count the area of PV panels in a PV electric field, although there are literature on PV panel fault detection, only the surface fault identification of PV panels is tested, while the internal faults (such as panel bad points or bad lines) cannot be identified because of the limitations of optical remote sensing. In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep learning technology are used to achieve rapid and accurate fault identification and localization of PV panel arrays. It can not only automatically identify PV panels that are obscured by dust and foreign objects, but also locate PV panels that have bad dots or bad lines, which greatly improves the ability and effectiveness of remote sensing PV panel fault monitoring. The high-resolution unmanned air vehicle (UAV) optical image and thermal infrared image are applied in this experiment. The Mask RCNN algorithm is used to accurately locate and number the photovoltaic panel of the optical image. Then, the fault scene classification model is established for the multi-type fault characteristics of the optical image and thermal infrared image within the panel range, so as to identify five types of faults, such as dust cover, branch cover, bird droppings cover, internal bad points and bad lines of PV panel, which effectively solves the problem that the single optical remote sensing image cannot identify the internal component faults of the photovoltaic panel under normal conditions.

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Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach

Cited by 6 publications

References 50 publications

Detection Criterion for Progressive Faults in Photovoltaic Modules Based on Differential Voltage Measurements

Detection Criterion for Progressive Faults in Photovoltaic Modules Based on Differential Voltage Measurements

Autonomous Monitoring and Analysis of Photovoltaic Systems

Study on Fault Monitoring Technology of Photovoltaic Panel Based on Thermal Infrared and Optical Remote Sensing

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