Cardinale-Villalobos Leonardo, Jimenez-Delgado Efren, García-Ramírez Yariel, Araya-Solano Luis, Solís-García Luis Antonio, Méndez-Porras Abel, Alfaro-Velasco Jorge
School of Electronic Engineering, Costa Rica Institute of Technology, Cartago 159-7050, Costa Rica.
School of Computer Engineering, Costa Rica Institute of Technology, Cartago 159-7050, Costa Rica.
Sensors (Basel). 2023 Jul 28;23(15):6749. doi: 10.3390/s23156749.
Infrared thermography (IRT) is a technique used to diagnose Photovoltaic (PV) installations to detect sub-optimal conditions. The increase of PV installations in smart cities has generated the search for technology that improves the use of IRT, which requires irradiance conditions to be greater than 700 W/m2, making it impossible to use at times when irradiance goes under that value. This project presents an IoT platform working on artificial intelligence (AI) which automatically detects hot spots in PV modules by analyzing the temperature differentials between modules exposed to irradiances greater than 300 W/m2. For this purpose, two AI (Deep learning and machine learning) were trained and tested in a real PV installation where hot spots were induced. The system was able to detect hot spots with a sensitivity of 0.995 and an accuracy of 0.923 under dirty, short-circuited, and partially shaded conditions. This project differs from others because it proposes an alternative to facilitate the implementation of diagnostics with IRT and evaluates the real temperatures of PV modules, which represents a potential economic saving for PV installation managers and inspectors.
红外热成像技术（IRT）是一种用于诊断光伏（PV）装置以检测次优条件的技术。智能城市中光伏装置的增加促使人们寻找能够改进IRT使用的技术，而IRT需要辐照度条件大于700 W/m²，这使得在辐照度低于该值时无法使用。该项目提出了一个基于人工智能（AI）的物联网平台，通过分析暴露于大于300 W/m²辐照度下的光伏组件之间的温差，自动检测光伏组件中的热点。为此，在一个诱导产生热点的实际光伏装置中对两种人工智能（深度学习和机器学习）进行了训练和测试。该系统在脏污、短路和部分遮挡条件下能够以0.995的灵敏度和0.923的准确率检测热点。该项目与其他项目的不同之处在于，它提出了一种便于实施IRT诊断的替代方案，并评估了光伏组件的实际温度，这为光伏装置管理者和检查员带来了潜在的经济节约。
