Fast calibration of heliostats

We present the HelioPoint method - a fast airborne method for calibrating entire heliostat fields

Association between Variants on Chromosome 4q25, 16q22 and 1q21 and Atrial Fibrillation in the Polish Population

Genome-wide studies have shown that polymorphisms on chromosome 4q25, 16q22 and 1q21 correlate with atrial fibrillation (AF). However, the distribution of these polymorphisms differs significantly among populations.To test the polymorphisms on chromosome 4q25, 16q22 and 1q21 in a group of patients (pts) that underwent catheter ablation of AF.Four hundred and ten patients with AF that underwent pulmonary vein isolation were included in the study. Control group (n = 550) was taken from healthy population, matched for age, sex and presence of hypertension. All participants were genotyped for the presence of the rs2200733, rs10033464, rs17570669, rs3853445, rs6838973 (4q25), rs7193343 (16q22) and rs13376333 (1q21) polymorphisms.All the polymorphisms tested (except rs17570669) correlated significantly with AF in univariate analysis (p values between 0.039 for rs7193343 and 2.7e-27 for rs2200733), with the odds ratio (OR) 0.572 and 0.617 for rs3853445 and rs6838973, respectively (protective role) and OR 1.268 to 3.52 for the other polymorphisms. All 4q25 SNPs tested but rs3853445 were independently linked with AF in multivariate logistic regression analysis. In haplotype analysis six out of nine 4q25 haplotypes were significantly linked with AF. The T allele of rs2200733 favoured increased number of episodes of AF per month (p = 0.045) and larger pulmonary vein diameter (recessive model, p = 0.032).Patients qualified for catheter ablation of AF have a significantly higher frequency of 4q25, 16q22 and 1q21 variants than the control group. The T allele of rs2200733 favours larger pulmonary veins and increased number of episodes of AF

A Benchmark of Simple Measurement Systems for Direct Irradiance

Accurate direct normal irradiance (DNI) measurements are essential for the design and the operation of concentrating solar power systems. Several measurement systems for DNI are available to users, but all commonly used systems still have drawbacks. Sun trackers with pyranometers and a pyrheliometer are expensive and require permanent checks and maintenance by qualified personnel, for example due to tracking errors and soiling effects. Simpler, i.e. more economic and robust sensors may have shortcomings regarding accuracy under various atmospheric conditions and might not be significantly less susceptible to soiling and user errors. Validations and benchmarking of simple radiometers for solar energy applications have been presented. To the best of our knowledge, no benchmarking study is available which evaluates some more recent simple measurement systems which are relevant for solar applications in 2023. Furthermore, most previous benchmarking studies did not measure atmospheric parameters like circumsolar irradiance which may directly influence the measurements of these sensors. We close this gap by benchmarking relevant measurement systems (Rotating Shadowband Irradiometer RSI and Rotating Shadowband Pyranometer RSP 4G; Delta-T SPN1, EKO MS-90, PyranoCam, Sunto CaptPro) at multiple sites. We also evaluate the influence of relevant atmospheric parameters which we measure with dedicated instruments at one site. We include the PyranoCam system in our benchmarking, a novel radiometer system suitable for all solar irradiance components including DNI. It consists of a pyranometer and a fisheye camera that takes photos of the whole sky and employs a combined physical and machine-learning model. The results of the study provide improved estimates of the sensors’ accuracies for a specific application and climatic condition and can assist in the development of corrections for the sensor technologies

Development of A Machine-Learning-Based Correction for Cloud-Camera-Based Solar Radiation Measurement

Accurate measurements of direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) are of great interest for solar energy applications. However, the established measurement techniques have at least one of the following drawbacks: high acquisition costs, intensive maintenance, or susceptibility to increased deviations. To counteract these shortcomings, cloud-camera-based solar radiation measurement is currently being investigated at the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR). In this context, a physics-based measurement method, called PyranoCam, using a system consisting of a pyranometer and an all-sky imager (ASI) was recently presented. Here, global horizontal irradiance (GHI) is measured by the pyranometer and DHI and DNI are derived by merging the information from the two sensors. The method is comparatively inexpensive and robust, but still shows potential in terms of accuracy of the irradiance components. In this thesis, the PyranoCam method is further developed and supplemented with a correction using techniques from the field of machine learning (ML). The intermediate PyranoCam results are analyzed, subjected to a selection process, and compiled as tabular features. The images from the ASI are undistorted and cropped. An ML model is suggested as follows to combine the tabular features with the image information relevant to the correction. A convolutional neural network (CNN) extracts relevant image features. Transfer learning and self-supervised learning approaches are used to prepare the CNN for this task. The tabular features are then merged with the extracted image features from the CNN, and based on this, a multilayer perceptron (MLP) predicts DHI. The model is end-to-end, implying that the CNN and the MLP are trained jointly. A suitable model architecture and optimal training settings are determined by experimentally comparing different candidate models. The further developed measurement method estimates DHI and DNI with significantly higher accuracy than the PyranoCam method. Based on the considered test data, for 1 min average DHI, the approach achieves a reduction of relative root-meansquare deviation (rRMSD) from 10.3% to 3.7% compared to PyranoCam. For 1 min DNI, calculated from predicted DHI and the pyranometer’s GHI measurement, a reduction in rRMSD from 6.4% to 3.4% is observed. The improvement is similarly observed for the test subsets of three sites throughout Europe, one of which was not included in the training of the model. The further developed method thus demonstrates transferability to other locations and competitiveness with already established measurement techniques

Towards deep learning based airborne monitoring methods for heliostats in solar tower power plants

While deep learning methods have proven their superiority over conventional image processing techniques in many domains, their use in airborne heliostat monitoring remains limited. Our aim is to bridge this gap by developing models to improve and extend existing image-based measurement methods in this field. We use Blender and BlenderProc to generate synthetic image data, which grants us access to vast amounts of training data essential for developing effective deep learning models. The exemplary model we train can potentially solve the following tasks related to airborne heliostat field monitoring: detection of heliostats and detection of mirror facet corners. Our promising preliminary results demonstrate the applicability of our approach to use synthetic training data for the development of the intended deep learning models

Fast airborne calibration of entire solar fields of heliostats

We present a fast airborne method for calibrating entire heliostat fields

Characteristics of study and control groups.

<p>Data presented as: n (%).</p><p>*mean (SD).</p><p>SD – standard deviation.</p><p>ND – no data; NA – not applicable.</p

Haplotypes found for 4q25 SNPs with independent effects on AF risk (i.e. rs2200733-rs10033464-rs17570669-rs6838973 haplotypes).

<p>Haplotypes found for 4q25 SNPs with independent effects on AF risk (i.e. rs2200733-rs10033464-rs17570669-rs6838973  haplotypes).</p

Pairwise linkage disequilibrium (LD) scores (r2) among 4q25 SNPs.

<p>Pairwise linkage disequilibrium (LD) scores (r2) among 4q25 SNPs.</p