Fault detection in solar arrays based on an efficiency threshold

Murillo-Soto, Luis Diego; Meza, Carlos

doi:10.1109/lascas45839.2020.9069046

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…This method was based on comparing the real generated power and the estimated theoretical power. The model used to estimate the generated power is the one proposed by [19]; the model starts from estimating the efficiency with Equation (1).…”

Section: Description For Electrical Measurementsmentioning

confidence: 99%

Quantitative Comparison of Infrared Thermography, Visual Inspection, and Electrical Analysis Techniques on Photovoltaic Modules: A Case Study

et al. 2022

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This paper compares multiple techniques to detect suboptimal conditions in the PV system. Detection of suboptimal conditions in the PV system is required to achieve optimal photovoltaic (PV) systems. Therefore, maintenance managers need to choose the most suitable techniques objectively. However, there is a lack of objective information comparing the effectiveness of the methods. This article calculates and compares the effectiveness of Infrared thermography (IRT), visual inspection (VI), and electrical analysis (EA) in detecting soiling, partial shadows, and electrical faults experimentally. The results showed that the VI was the best at detecting soiling and partial shading with 100% of effectiveness. IRT and EA had an effectiveness of 78% and 73%, respectively, detecting the three types of conditions under study. It was not possible to achieve maximum detection using only one of the techniques, but that VI must be combined with IR or EA. This research represents a significant contribution by achieving an objective comparison between techniques for detecting suboptimal conditions, being very useful to guide PV system maintainers and designers of fault detection techniques.

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Section: Description For Electrical Measurementsmentioning

confidence: 99%

Quantitative Comparison of Infrared Thermography, Visual Inspection, and Electrical Analysis Techniques on Photovoltaic Modules: A Case Study

et al. 2022

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“…Imaging methods are currently expensive and time consuming, whereas electrical characterization is cheaper and more flexible [24]. The latter methods also can be further split into several branches: signal processing and statistical methods (e.g., [8,25,26]), i − v curve characteristics analysis (e.g., [27,28]), power losses analysis or efficiency analysis (e.g., [17,29,30]), current and/or voltage measurements (e.g., [31,32]), and artificial intelligent methods (e.g., [9,33,34]). The diagnosis algorithm implemented in this work is based on the IF-THEN rules presented by the authors in [35] and follows the diagnosis branch based on current-voltage measurements.…”

Section: Automated Fault Management System Approachmentioning

confidence: 99%

“…Nevertheless, image-based fault detection techniques are not suitable for real-time reconfiguration given that it requires a complex data communication network with a relatively large bandwidth. Fault detection techniques based on measurement of electrical variables such as [17,18] are most suitable for a mitigating faults with DRS.…”

Section: Introductionmentioning

confidence: 99%

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

Murillo-Soto

Meza

2021

Energies

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This work proposes an automated reconfiguration system to manage two types of faults in any position inside the solar arrays. The faults studied are the short-circuit to ground and the open wires in the string. These faults were selected because they severely affect power production. By identifying the affected panels and isolating the faulty one, it is possible to recover part of the power loss. Among other types of faults that the system can detect and locate are: diode short-circuit, internal open-circuit, and the degradation of the internal parasitic serial resistance. The reconfiguration system can detect, locate the above faults, and switch the distributed commutators to recover most of the power loss. Moreover, the system can return automatically to the previous state when the fault has been repaired. A SIMULINK model has been built to prove this automatic system, and a simulated numerical experiment has been executed to test the system response to the faults mentioned. The results show that the recovery of power is more than 90%, and the diagnosis accuracy and sensitivity are both 100% for this numerical experiment.

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“…The operation and maintenance activities in photovoltaic (PV) plants can benefit from an accurate model of the expected PV plant's power or current efficiency. PV operators can identify underperformance due to failures or soiling with this efficiency model, proposing new techniques such as [1][2][3]. Even though a PV module datasheet provides information that can be used to get an idea of the electrical characteristics of a PV plant, that information has been obtained under laboratory conditions which, in several cases, are different from outdoor conditions.…”

Section: Introductionmentioning

confidence: 99%

Validation of an outdoor efficiency model for photovoltaic modules

Murillo-Soto

Meza²

2022

Rev.Fac.Ing.Univ.Antioquia

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The study of the efficiency of photovoltaic modules in outdoor conditions allows determining their correct operation and to detect abnormal behavior. Most efficiency models developed use experimental data that are difficult to obtain as they require specialized equipment and a long measurement time under laboratory conditions to identify or adjust the model parameters. In this article, we propose a linear equation that estimates the efficiency of photovoltaic modules using irradiance and back panel temperatura measurements as input variables. The main contribution is that the parameters of the proposed model can be obtained directly from the reference information (IEC61853-1 power values) without any data regression. The proposed model was validated using experimental data obtained from three different climatic zones during a whole year. The model shows the best fit to the data of four analyzed models, and its normalized root mean square deviation for all photovoltaic modules is less than 3.6%; this dispersion could be explained by the fact that the outdoor data have an uncertainty of around 3.0%

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Fault detection in solar arrays based on an efficiency threshold

Cited by 8 publications

References 15 publications

Quantitative Comparison of Infrared Thermography, Visual Inspection, and Electrical Analysis Techniques on Photovoltaic Modules: A Case Study

Quantitative Comparison of Infrared Thermography, Visual Inspection, and Electrical Analysis Techniques on Photovoltaic Modules: A Case Study

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

Validation of an outdoor efficiency model for photovoltaic modules

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