Real-time junction temperature estimation model of photovoltaic modules for determining application scenarios. (April 2023)

• Record Type: Journal Article
• Authors: Mo, Qiu; Xiong, Weiming; Li, Weiqiang
• Is Part Of: Solar energy. Volume 254(2023)
• Page Start: 223
• Page End: 234
• Publication Date: 2023-04
• Subjects: Junction temperature -- Photovoltaic (PV) modules -- Radiative cooling -- Thermal delay effect -- Coincidence probability; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2023.03.015
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: We selected an adequate junction temperature estimation model and optimized key variables in a step-by-step manner. We analyzed statistically the dispersion degree of the PV module's operating temperature. The application scope and scenarios of the model were determined. We have verified the process at an existing PV station in Zhejiang Province, China. Abstract: Junction temperature is an important factor affecting the power generation efficiency and service life of photovoltaic (PV) modules, and it is also the main parameter for calculating temperature rise loss of PV stations. At present, the real-time junction temperature estimation models of PV modules under natural environmental conditions reveal such problems as low accuracy and restricted application scenarios. In this paper, we propose a method which requires the following actions: analyzing the influence factors of the junction temperature of PV modules; selecting an adequate junction temperature estimation model based on the main influence factors (such as radiative cooling, wind speed, irradiance and thermal delay effect) and the capability of PV stations to obtain calculation parameters; optimizing and selecting the key variables step by step that are involved in the model. Then, we analyze the coincidence probabilities of the model under different environmental conditions and the discrete degree of the operating temperature of PV modules to obtain ideal application scenarios of the model. This provides an accurate reference for on-site engineers to apply the model. The example verification shows that the coincidence probabilities of the real-time junction temperature estimation model within the error range of ±1.5 °C, ±2.5 °C, ±3.5 °C and ±5.0 °C reach 72.61 %, 88.65 %, 95.29 % and 98.51 %, respectively, which features high estimation accuracy, convenient use, high efficiency and wide application scenarios.