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Smart city article details
| Title | Detection Of Suboptimal Conditions In Photovoltaic Systems Integrating Data From Several Domains |
|---|---|
| ID_Doc | 19286 |
| Authors | Cardinale-Villalobos L.; Murillo-Soto L.D.; Jimenez-Delgado E.; Sequeira J.A. |
| Year | 2024 |
| Published | Communications in Computer and Information Science, 1938 CCIS |
| DOI | http://dx.doi.org/10.1007/978-3-031-52517-9_2 |
| Abstract | Researchers have been exploring methods to detect suboptimal conditions in photovoltaic (PV) modules, such as visual inspection, electrical analysis, and thermography. Each method has its advantages and limitations. To enhance the accuracy and efficiency of detecting these conditions, this research proposed an integrated Internet of Things (IoT) platform called the Multi-method system (MMS). This platform combines Infrared Thermography (IRT), visual inspection using RGB image processing, and electrical analysis. The MMS enables automated data capture, analysis, and remote accessibility. To validate the system, an experiment was conducted at the Costa Rica Institute of Technology. The MMS effectively detected suboptimal conditions due to soiling, partial shading, and short circuits. The system achieved a sensitivity of 0.97%, an accuracy of 0.98%, and a precision of 100%. This project functions as a proof of concept, in this case limited to a fixed location and for specific suboptimal conditions, however, it represents a solution with potential for industrial use. The research contributes to advancing PV system reliability and performance monitoring in Smart Cities, offering implications for improving solar energy efficiency and reducing maintenance costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
| Author Keywords | CNN; Deep Learning; Fault Detection Technique; Internet of things platform; Photovoltaic |
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