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This paper summarizes the trends in metallization and interconnection technology in the eyes of the participants of the 8th Metallization and Interconnection Workshop. Participants were asked in a questionnaire to share their view on the future development of metallization technology, the kind of metal used for front side metallization and the future development of interconnection technology. The continuous improvement of the screen-printing technology is reflected in the high expected percentage share decreasing from 88% in three years to still 70% in ten years. The dominating front side metal in the view of the participants will be silver with an expected percentage share of nearly 70% in 2029. Regarding interconnection technologies, the experts of the workshop expect new technologies to gain significant technology shares faster. Whereas in three years soldering on busbars is expected to dominate with a percentage share of 71% it will drop in ten years to 35% in the eyes of the participants. Multiwire and shingling technologies are seen to have the highest potential with expected percentage shares of 33% (multiwire) and 16% (shingling) in ten years.
Summary of the 8th Workshop on Metallization and Interconnection for Crystalline Silicon Solar Cells
(2019)
This article gives a summary of the 8th Metallization and Interconnection workshop and attempts to place each contribution in the appropriate context. The field of metallization and interconnection continues to progress at a very fast pace. Several printing techniques can now achieve linewidths below 20 μm. Screen printing is more than ever the dominating metallization technology in the industry, with finger widths of 45 μm in routine mass production and values below 20 μm in the lab. Plating technology is also being improved, particularly through the development of lower cost patterning techniques. Interconnection technology is changing fast, with introduction in mass production of multiwire and shingled cells technologies. New models and characterization techniques are being introduced to study and understand in detail these new interconnection technologies.
Some 165 global experts and specialists from industry and academic institutes met at the 8th Metallization & Interconnection Workshop (MIW2019) that took place from 13 to 14 May 2019 in Konstanz, Germany. Participants from 19 countries debated results of 28 oral and 11 poster presentations.
All presentations are available on www.metallizationworkshop.info as pdf documents. As in previous editions, lots of room was available for discussions and networking during the two-days program which included panel and market-place discussions as well as social events (reception, workshop dinner).
These proceedings contain: a summary of the oral and poster presentations, the results of the survey conducted during the workshop, and peer-reviewed papers based on workshop contributions.
Das hier vorgestellte Netzoptimierungstool kann dem Verteilnetzbetreiber bei einem Störfall im Netz in Echtzeit eine Lösung zur Steuerung seiner Betriebsmittel vorschlagen. Dadurch kann das bestehende Netz optimal genutzt werden und ein kostenintensiver Netzausbau im Mittel- und Niederspannungsnetz verringert oder sogar verhindert werden. Als Grundlage für den Netzoptimierer dient ein künstliches neuronales Netz (KNN). Zum Training des KNN wurden Störfälle generiert, die auf reellen Erzeugungs- und Lastprofilen aus dem CoSSMic-Projekt basieren [1]. Für jeden Störfall wurde aus allen möglichen und sinnvollen Netzkonfigurationen eine optimierte Netztopologie anhand von Lastflussberechnungen ermittelt. Durch die Variation der Stufenschalter der Transformatoren und der Stellungen aller installierten Schalter im Netz wurde berechnet, wie der Stromfluss gelenkt werden muss, damit keines der Betriebsmittel die zulässigen Belastungsgrenzen mehr überschreitet. Für ein virtuelles Testnetz konnte mit einem trainierten KNN zu 90 Prozent die optimale Lösung des jeweiligen Störfalls erkannt werden. Durch die Anwendung der N-Best Methode konnte die Vorhersagewahrscheinlichkeit auf annähernd 99 Prozent erhöht werden.
Das Potential der Offshore-Windenergie, welches hauptsächlich auf hohe mittlere Windgeschwindigkeiten zurückzuführen ist, kann nicht ignoriert werden. Trotzdem zeigt die Betrachtung der aktuell installierten Leistung und der Stromgestehungskosten, dass zusätzliche Risiko- und Kostenfaktoren existieren. Diese sind vor allem auf die Installation, die Energiewandlersysteme und die Netzanbindung zurückzuführen. Getriebeschäden sind einer dieser großen Kostenfaktoren. Aus diesem Grund gewinnen getriebelosen Windkraftanlagen mit permanentmagneterregten Synchrongeneratoren immer mehr an Relevanz. In der Netzanbindung von ganzen Offshore-Windparks überwiegt die Hochspannungs-Gleichstrom-Übertragung (HGÜ) ab einer Übertragungsdistanz von 80 km. Diese Tendenz ist sinkend. Steigende windparkinterne Spannungen auf 66 kV fördern zusätzlich den Verzicht auf Umspannplattformen, welche für die HGÜ-Technik aktuell sinnvoll sind. Diese und weitere bereits in Aussicht stehenden Entwicklungen führen zu einer Einschränkung der Risiko- und Kostenfaktoren. Es wird demnach davon ausgegangen, dass die Offshore-Windenergie, als Ergänzung zur Onshore-Windenergie, eine wichtige Rolle im Rahmen der Energiewende einnimmt.
We present an innovative decision support system (DSS) for distribution system operators (DSO) based on an artificial neural network (ANN). A trained ANN has the ability to recognize problem patterns and to propose solutions that can be implemented directly in real time grid management. The principle functionality of this ANN based optimizer has been demonstrated by means of a simple virtual electrical grid. For this grid, the trained ANN predicted the solution minimizing the total line power dissipation in 98 percent of the cases considered. In 99 percent of the cases, a valid solution in compliance with the specified operating conditions was found. First ANN tests on a more realistic grid, calibrated with household load measurements, revealed a prediction rate between 88 and 90 percent depending on the optimization criteria. This approach promises a faster, more cost-efficient and potentially secure method to support distribution system operators in grid management.
We present an alternative approach to grid management in low voltage grids by the use of artificial intelligence. The developed decision support system is based on an artificial neural network (ANN). Due to the fast reaction time of our system, real time grid management will be possible. Remote controllable switches and tap changers in transformer stations are used to actively manage the grid infrastructure. The algorithm can support the distribution system operators to keep the grid in a safe state at any time. Its functionality is demonstrated by a case study using a virtual test grid. The ANN achieves a prediction rate of around 90% for the different grid management strategies. By considering the four most likely solutions proposed by the ANN, the prediction rate increases to 98.8%, with a 0.1 second increase in the running time of the model.
This thesis deals with the object tracking problem of multiple extended objects. For instance, this tracking problem occurs when a car with sensors drives on the road and detects multiple other cars in front of it. When the setup between the senor and the other cars is in a such way that multiple measurements are created by each single car, the cars are called extended objects. This can occur in real world scenarios, mainly with the use of high resolution sensors in near field applications. Such a near field scenario leads a single object to occupy several resolution cells of the sensor so that multiple measurements are generated per scan. The measurements are additionally superimposed by the sensor’s noise. Beside the object generated measurements, there occur false alarms, which are not caused by any object and sometimes in a sensor scan, single objects could be missed so that they not generate any measurements.
To handle these scenarios, object tracking filters are needed to process the sensor measurements in order to obtain a stable and accurate estimate of the objects in each sensor scan. In this thesis, the scope is to implement such a tracking filter that handles the extended objects, i.e. the filter estimates their positions and extents. In context of this, the topic of measurement partitioning occurs, which is a pre-processing of the measurement data. With the use of partitioning, the measurements that are likely generated by one object are put into one cluster, also called cell. Then, the obtained cells are processed by the tracking filter for the estimation process. The partitioning of measurement data is a crucial part for the performance of tracking filter because insufficient partitioning leads to bad tracking performance, i.e. inaccurate object estimates.
In this thesis, a Gaussian inverse Wishart Probability Hypothesis Density (GIW-PHD) filter was implemented to handle the multiple extended object tracking problem. Within this filter framework, the number of objects are modelled as Random Finite Sets (RFSs) and the objects’ extent as random matrices (RM). The partitioning methods that are used to cluster the measurement data are existing ones as well as a new approach that is based on likelihood sampling methods. The applied classical heuristic methods are Distance Partitioning (DP) and Sub-Partitioning (SP), whereas the proposed likelihood-based approach is called Stochastic Partitioning (StP). The latter was developed in this thesis based on the Stochastic Optimisation approach by Granström et al. An implementation, including the StP method and its integration into the filter framework, is provided within this thesis.
The implementations, using the different partitioning methods, were tested on simulated random multi-object scenarios and in a fixed parallel tracking scenario using Monte Carlo methods. Further, a runtime analysis was done to provide an insight into the computational effort using the different partitioning methods. It emphasized, that the StP method outperforms the classical partitioning methods in scenarios, where the objects move spatially close. The filter using StP performs more stable and with more accurate estimates. However, this advantage is associated with a higher computational effort compared to the classical heuristic partitioning methods.