Abstract
In this paper, an Efficient Method for PHotovoltaic Arrays Study through Infrared Scanning (EMPHASIS) is presented; it is a fast, simple, and trustworthy cell-level diagnosis method for commercial photovoltaic (PV) panels. EMPHASIS processes temperature maps experimentally obtained through IR cameras and is based on a power balance equation. Along with the identification of malfunction events, EMPHASIS offers an innovative feature, i.e., it estimates the electrical powers generated (or dissipated) by the individual cells. A procedure to evaluate the accuracy of the EMPHASIS predictions is proposed, which relies on detailed three-dimensional (3-D) numerical simulations to emulate realistic temperature maps of PV panels under any working condition. Malfunctioning panels were replicated in the numerical environment and the corresponding temperature maps were fed to EMPHASIS. Excellent results were achieved in both the cell- and panel-level power predictions. More specifically, the estimation of the power production of a PV panel with a shunted cell demonstrated an error lower than 1%. In cases of strong nonuniformities as a PV panel in hotspot, an estimation error in the range of 9–16% was quantified.
Highlights
In the last decades, both the growing efficiency and the cost reduction in photovoltaic (PV) plants have contributed to their wide diffusion in customer applications [1]
To carry out a cell-level power assessment, the method in [40] was based on a power balance law applied to on-field temperature maps of PV panels under their actual working conditions; this was possible since the approach does not demand the stop of the PV plant power production
The accuracy of the method predictions has been evaluated through a strategy relying on “simulated experiments,” which are not to be replicated by the EMPHASIS end users
Summary
Both the growing efficiency and the cost reduction in photovoltaic (PV) plants have contributed to their wide diffusion in customer applications [1]. In [31,32], the IR images are fed to heuristics methods, whereas neural networks are adopted in [37,38,39] These strategies (i) rely on time-demanding preliminary stages (e.g., the training of neural networks), (ii) require the use of additional sensors, and (iii) may partially interrupt the power production of the plant. To carry out a cell-level power assessment, the method in [40] was based on a power balance law applied to on-field temperature maps of PV panels under their actual working conditions; this was possible since the approach does not demand the stop of the PV plant power production. A new version of the same method requiring a reduced number of sensors was presented in [41] Both approaches allowed identifying localized faults, their cell-level electrical power estimations were still fairly imprecise.
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