A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants
| العنوان: | A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants |
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| المؤلفون: | Clayton H. da Costa, Robson R. Linhares, Gilberto Lexinoski, André Eugênio Lazzaretti, Elder Oroski, Rafael Eleodoro de Goes, Paulo Cézar Stadzisz, Rodrigo Braun dos Santos, Guilherme Luiz Moritz, Júlio Shigeaki Omori, Guilherme D. Yamada, Marcelo P. Rodrigues |
| المصدر: | Sensors, Vol 20, Iss 4688, p 4688 (2020) Sensors Volume 20 Issue 17 Sensors (Basel, Switzerland) |
| بيانات النشر: | MDPI AG, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Power station, Computer science, 020209 energy, Real-time computing, Irradiance, monitoring systems, 02 engineering and technology, Fault (power engineering), lcsh:Chemical technology, Biochemistry, Article, Fault detection and isolation, Analytical Chemistry, Plant efficiency, 0202 electrical engineering, electronic engineering, information engineering, PV plants, lcsh:TP1-1185, Electrical and Electronic Engineering, Instrumentation, fault classification, business.industry, 020208 electrical & electronic engineering, Fossil fuel, Photovoltaic system, Atomic and Molecular Physics, and Optics, fault detection, Power (physics), embedded systems, business, Energy (signal processing) |
| الوصف: | Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1424-8220 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9695308651620d387069fe55ed59310aTest https://www.mdpi.com/1424-8220/20/17/4688Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....9695308651620d387069fe55ed59310a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14248220 |
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