Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells

Abstract

Solar Photovoltaic (PV) energy has experienced an important growth and prospect during the last decade due to the constant development of the technology and its high reliability, together with a drastic reduction in costs. This fact has favored both its large-scale implementation and small-scale Distributed Generation (DG). PV systems integrated into local distribution systems are considered to be one of the keys to a sustainable future built environment in Smart Cities (SC). Advanced Operation and Maintenance (O&M) of solar PV plants is necessary. Powerful and accurate data are usually obtained on-site by means of current-voltage (I-V) curves or electroluminescence (EL) images, with new equipment and methodologies recently proposed. In this work, authors present a comparison between five AI-based models to classify PV solar cells according to their state, using EL images at the PV solar cell level, while the cell I-V curves are used in the training phase to be able to classify the cells based on its production efficiency. This automatic classification of defective cells enormously facilitates the identification of defects for PV plant operators, decreasing the human labor and optimizing the defect location. In addition, this work presents a methodology for the selection of important variables for the training of a defective cell classifier.

Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage

“…Reliable forecasting of solar radiation intensity two days ahead is possible thanks to simple solutions such as UTSA SkyImager for imaging the entire sky and (after supplementing with AI algorithms: DL and gradient boosted trees (GBT)) for forecasting radiation intensity from the sub-image surrounding the sun [42]. AI-based models allow the classification of the technical condition of PV cells based on electroluminescence images at the level of PV cells and current-voltage curves of PV cells to classify cells based on their production efficiency [43].…”

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

“…Te solar PV energy system's constant development and cost-reduction benefts have brought it into a popular renewable energy solution for large-scale implementation or small-scale distributed generation (DG) [1]. In 2022, the increased capacity of solar PV accounts for 220 GW, which is more than half of the total increase in renewable energy, namely, 340 GW.…”