Refine
Document Type
- Article (2)
- Bachelor Thesis (1)
- Part of a Book (1)
- Conference Proceeding (1)
Language
- English (5) (remove)
Is part of the Bibliography
- no (5)
Institute
- Fachbereich 1 - Institut Energiesysteme und Energiewirtschaft (5) (remove)
Efficient and reliable onsite inspection methods are gaining importance as the construc-tion of PV power plants is expanding. For large PV installations, time- and cost-efficient failure detection is essential for optimized operation and maintenance. For this purpose, various optical methods as Infrared thermography (IR), Electroluminescence (EL), Pho-toluminescence (PL) and Ultraviolet Fluorescence (UVF) are employed and under con-stant development. For each method, the camera, and eventually the light source, can be handheld, or mounted on a drone, also called unmanned aircraft vehicle (UAV), to achieve higher throughputs.
IR is the most widely used optical onsite PV inspection method, as many defects can be detected by the thermal radiation (heating) of the defect component. EL and PL reveal further information on the electrical behaviour of the Si-waver. They are also widely used and take the role of a complement to IR, showing electrically active/inactive areas of the semiconductor. On the other hand, UVF focuses on the degradation of the polymeric encapsulant of the Si-cell, most commonly consisting of EVA (ethylene-vinyl acetate). The degradation of the encapsulant can lead to its discoloration, also called yellow-ing/browning, which decreases the transmittance of visual light. UVF patterns can show this yellowing as well as humidity and oxygen entrances, which can lead to effects of corrosion. Both mechanisms (discoloration and corrosion) decrease the performance of the PV cell. The discoloration cannot be directly observed on IR or EL images, as the encapsulant is neither a heat source nor electroconductive. Using IR imagery, severe discoloration might be observed indirectly, as the reduced optical transmittance leads to changes in heat transfer mechanisms concerning the cell and the encapsulant.
Similarly, as long as corrosion does not lead to inactive cell areas or heating, it most likely will not be spotted using EL, PL or IR. So, UVF can fill the niche of inspecting the state of the encapsulant and detecting its defects due to climate impacts in early stages.
While a high number of studies on IR, EL, PL and some on UVF were performed in Europe and the USA, there are not yet many studies about the application of these tech-niques in South America (i.e., in Brazil). UVF mainly depends on climate factors (irradi-ation, temperature, humidity) and the operation time/”age” of the module. The UVF im-agery method has not yet been tested in climate and system conditions of Brazil. Fur-thermore, systems in Brazil are more recently installed. All this can affect differences in the results of UVF imagery applied in Europe, the USA and Brazil.
The present work focuses on the application of UVF imaging on PV power plants in Bra-zil, the creation of an experimental setup and the proposal of proceedings for the data analysis of the acquired images. The aim is to propose a method that is suitable for large-scale inspection.
Object detection systems which operate on large data streams require an efficient scaling with available computation power. We analyze how the use of tile-images can increase the efficiency (i.e. execution speed) of distributed HOG-based object detectors. Furthermore we discuss the challenges of using our developed algorithms in practical large scale scenarios. We show with a structured evaluation that our approach can provide a speed-up of 30-180 % for existing architectures. Due to the its generic formulation it can be applied to a wide range of HOG-based (or similar) algorithms. In this context we also study the effects of applying our method to an existing detector and discuss a scalable strategy for distributing the computation among nodes in a cluster system.
In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.
While more and more nuclear installations facing the end of their lifetime, decommissioning financing issues gain importance in political discussions.
The financing needs are huge along the Uranium value chain. Following the polluter pays principle the operator of a nuclear installation is expected to accumulate all the necessary decommissioning funds during the operating life of its facility. However, since decommissioning experience is still limited,
since the decommissioning process can take several decades and since the time
period between the shutdown of a nuclear installation and the final disposal of radioactive waste can be very long, there are substantial risks that costs will be underestimated and that the liable party and the funds accumulated might
not be available anymore when decommissioning activities have to be paid.
Nevertheless, these financing risks can be reduced by the implementation of transparent, restricted, well-governed decommissioning financing schemes, with a system of checks and balances that aims at avoiding negative effects
stemming from conflicts of interests.