Segmentation of Photovoltaic Module Cells in Electroluminescence Images

High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kinds of defects, which is time-consuming and expensive. Automated segmentation of cells is therefore a key step in automating the visual inspection workflow. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. This enables controlled studies on large amounts of data to understanding the effects of module degradation over time-a process not yet fully understood. The proposed method infers in several steps a high-level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 94.47% and an $F_1$ score of 97.54%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks

Linear spatial hypothesis tests for random fields

We propose a statistical framework that tests a hypothesis about the functional properties of a random field. The framework is formulated for random fields with a compact d-dimensional Euclidean domain space, with a signal plus noise observation model. The test statistic has a linear form, and the hypothesis is formulated in terms of an integral operator applied to an unobserved signal. The test decision is performed using a single trajectory of a random field. Using the apparatus of Stieltjes integral, we prove functional limit theorems under the null and alternative hypotheses. To obtain those results we consider two different model assumptions on the noise random field. Our assumptions on the signal and test parameters feature the notion of the variation in the sense of Hardy and Krause. Extensive numerical simulations provide insights on various characteristics of the formulated tests. We present simulations under the null and alternative hypotheses, and compare power for different test parameters. This work is primarily motivated by an application in image processing in quality control of photovoltaic (PV) modules. Our framework allows identifying defects in such images. Furthermore, we propose a set of image preprocessing algorithms for such images

Encoder–Decoder Semantic Segmentation Models for Electroluminescence Images of Thin-Film Photovoltaic Modules

We consider a series of image segmentation methods based on the deep neural networks in order to perform semantic segmentation of electroluminescence (EL) images of thin-film modules. We utilize the encoder-decoder deep neural network architecture. The framework is general such that it can easily be extended to other types of images (e.g., thermography) or solar cell technologies (e.g., crystalline silicon modules). The networks are trained and tested on a sample of images from a database with 6000 EL images of copper indium gallium diselenide thin film modules. We selected two types of features to extract, shunts and so called “droplets.” The latter feature is often observed in the set of images. Several models are tested using various combinations of encoder-decoder layers, and a procedure is proposed to select the best model. We show exemplary results with the best selected model. Furthermore, we applied the best model to the full set of 6000 images and demonstrate that the automated segmentation of EL images can reveal many subtle features, which cannot be inferred from studying a small sample of images. We believe these features can contribute to process optimization and quality control

PV-AIDED: Photovoltaic Artificial Intelligence Defect Identification. Multichannel Encoder-decoder Ensemble Models for Electroluminescence Images of Thin-film Photovoltaic Modules, PEARL TF-PV.

The Solar-Era.net project PEARL TF-PV, [1], aims to reduce the uncertainties in the operation of thin-film solar power plants. To this end, one of the main parts of the project is the gathering of performance data and electroluminescence (EL) images of different types of thin-film solar cells and modules (see abstract of Mirjam Theelen et al, this conference). Detailed, local information on the module performance is obtained using EL imaging, which may provide early warning signs of degradation. A large number of samples (over 6000 modules) are analyzed, ranging from cells and modules produced in the different laboratories of the project partners to industrially produced modules used in power plants. Measurements are performed in laboratories as well as outdoor directly at the power plants location. All gathered data is stored in a database that in turn is used to develop a failure catalogue for thin-film modules that describes typical defects, visible with EL in various technologies, and their influence on the solar modules reliability and lifetime. In this work we present a novel image segmentation approach, aiming to identify commonly occurring defects in thin-film modules. We are building on top of the encoder-decoder neural networks framework, that have established itself as a standard tool in many other image processing applications. We demonstrate our software, PV-AIDED, is capable of fully automatic and fast EL image processing of full-sizes modules. We are able to reliably identify frequently occurring defects in thin-film modules, such as shunts and so called “droplets”. The framework is general and applicable to other types of defects, other types of PV images, as well as other types of PV technology