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

Statistical EL-Image Evaluation for Describing the Degradation of PV-Modules after a Hailstorm

El-images are used to visualize defects in solar modules. Quantitative and comparable figures to address and identify defective cells are not extracted from the images so far. We used a statistical approach to get three key figures from the images: defective status, power-relevant area, and cell degradation. For analysis several EL-images of the same module were necessary. To study the impact of wind loads on the crack structures, the modules were exposed to 23,300 mechanical loading cycles, mimicking wind loads up to 40 m/s. The evolution of the crack structures was studied based on the ELimages that were recorded occasionally. The image evaluation reveals that defective, power-reducing cells and nondefective cells can be distinguished. Cell changes could be identified. The impact on the module power was within the measurement accuracy

Computer Vision Tool for Detection, Mapping and Fault Classification of PV Modules in Aerial IR Videos

Increasing deployment of photovoltaics (PV) plants demands for cheap and fast inspection. A viable tool for this task is thermographic imaging by unmanned aerial vehicles (UAV). In this work, we develop a computer vision tool for the semi-automatic extraction of PV modules from thermographic UAV videos. We use it to curate a dataset containing 4.3 million IR images of 107842 PV modules from thermographic videos of seven different PV plants. To demonstrate its use for automated PV plant inspection, we train a ResNet-50 to classify ten common module anomalies with more than 90 % test accuracy. Experiments show that our tool generalizes well to different PV plants. It successfully extracts PV modules from 512 out of 561 plant rows. Failures are mostly due to an inappropriate UAV trajectory and erroneous module segmentation. Including all manual steps our tool enables inspection of 3.5 MW p to 9 MW p of PV installations per day, potentially scaling to multi-gigawatt plants due to its parallel nature. While we present an effective method for automated PV plant inspection, we are also confident that our approach helps to meet the growing demand for large thermographic datasets for machine learning tasks, such as power prediction or unsupervised defect identification

Impact of the module backsheet components on the electrical field performance of PV-plants

This study links chemical analysis of PV-module’s backsheet components with the power output of PV-systems during their lifetime. Several PV-systems all with two or more module types and also backsheet types were investigated. We identified three classes of backsheet types with respect to their influence on the module performance. While PV-modules with robust backsheets (e.g. double-fluoropolymers) perform constantly well over years, others show linear or even exponential performance losses, while others display total failure (e.g. single-fluoropolymers or non-fluoropolymers). Humidity is a most probable driving factor for the faster ageing process of modules with critical backsheets in terms of their chemical layer components and the resulting properties

Case study on the dependency of the degradation rate on degradation modes and methodology using monitoring data

We present here a degradation study of a PV power plant consisting of several module pairs connecting each to a micro-inverter with monitoring. The modules comprise cell cracks, cell breakages and edge shunting. The impact of using different methodologies on the resulting relative yield loss rates is analyzed. It is demonstrated how these methods can be altered to circumvent missing irradiation sensor data generating a substitute for a relative degradation rate. The relative degradation rate of cell cracks is close to the reference with all methods and near to the detection limit which indicates little degradation for cell cracks beyond the material-inherent degradation. The modules with cell breakages and edge shunting show a higher relative yield loss of l-3 %/a depending on the method of analysis. This indicates quite a variation is possible depending on the method thus warranting caution when evaluating degradation rates or their substitutes. It was shown that cell breakages perform increasingly worse in summer leading to even higher but seasonal performance losses. Because of that, the relative yield loss can be much higher than just the relative loss in rated power due to probably averse temperature effects on defect performance. To better assess cell crack degradation, either longer observation periods or more precise methods are neede

Verfahren und System zur Bewertung von Solarzellen

Die vorliegende Erfindung betrifft ein Verfahren für eine Bewertung der Qualität von Solarzellen einer Fotovoltaikanlage. Die Erfindung betrifft auch ein System zur Bewertung von Solarzellen. Fotovoltaikanlagen umfassen in der Regel eine Vielzahl von Solarmodulen. Ein Solarmodul ist eine bauliche Einheit, die eine Vielzahl von Solarzellen umfasst. Um hinreichend hohe elektrische Spannungen erzeugen zu können, sind Solarzellen eines Solarmoduls elektrisch seriell miteinander verbunden

A Self-Referencing Method for Detecting Underperforming Strings in MWp-PV-Generators

For optimization of operation and maintenance of PV power stations the knowledge (identification and localization) of poor-performing modules, strings and inverters is important. The developed self-referencing method analyzes monitoring data, with a particular focus on energy yield. First, a well-performing unit is identified, second, the other units are set into relation to the well-preforming unit. Without weather data and prior-knowledge of the PVsystem poor-performing units (strings, inverters) are identified in this fashion. Three case studies are presented. IRimages verified the failure types. Strings suffering from yield loss due to PID or bridged PV-modules where identified and the yield loss quantified. Strings in bottom rows perform less (-2.6%) than those in top rows. The noise is about 4%, which means, failures with power loss less than 4% are masked in the uncertainty level of the monitoring data, e. g. one bypassed substring, hot cells. Yield relevant module failures such as PID, bridged modules, several bypassed substrings, are identified and localized reliably using the self-referencing method

Solar‐NIRT: Identification of PV‐module backsheets in the field with natural sunlight

Reliable and durable solar power plants require PV modules with high-grade polymer encapsulants and backsheets (BSs). For performance analyses of PV installations fast, reliable and non-destructive methods for determining composition and degradation state of polymer components need to be developed. Here, we show that the structure of some common polymer BSs can be determined in the field in real time by analyzing near-infrared transmission (NIRT) spectra collected under illumination with natural sunlight. The potential of this “Solar-NIRT” method was probed by field measurements on a multi-MW PV power plant where four major BS types were identified by multispectral cross-sectional Raman imaging. Additionally, degradation of a particular BS type was found to result in distinct changes in NIRT spectra allowing the degraded BSs to be classified as a separate type. Principal component analysis (PCA) applied to a collection of 62 Solar-NIRT spectra allowed to create a map of five clusters, each corresponding to a particular BS type. The feasibility of using the PCA cluster map for the identification of unknown samples was shown on a test set of 13 different BSs. The Solar-NIRT is relatively fast, non-invasive, selective, can be upgraded to a non-contact regime making it a promising tool for high-throughput characterization