Analysis Of Residual Current Flows In Inverter Based Energy Systems Using Machine Learning Approaches

Faults and unintended conditions in grid-connected photovoltaic systems often cause a change of the residual current. This article describes a novel machine learning based approach to detecting anomalies in the residual current of a photovoltaic system. It can be used to detect faults or critical states at an early stage and extends conventional threshold-based detection methods. For this study, a power-hardware-in-the-loop approach was carried out, in which typical faults have been injected under ideal and realistic operating conditions. The investigation shows that faults in a photovoltaic converter system cause a unique behaviour of the residual current and fault patterns can be detected and identified by using pattern recognition and variational autoencoder machine learning algorithms. In this context, it was found that the residual current is not only affected by malfunctions of the system, but also by volatile external influences. One of the main challenges here is to separate the regular residual currents caused by the interferences from those caused by faults. Compared to conventional methods, which respond to absolute changes in residual current, the two machine learning models detect faults that do not affect the absolute value of the residual current

Thin metal layer as transparent electrode in n-i-p amorphous silicon solar cells

In this paper, transparent electrodes, based on a thin silver film and a capping layer, are investigated. Low deposition temperature, flexibility and low material costs are the advantages of this type of electrode. Their applicability in structured n-i-p amorphous silicon solar cells is demonstrated in simulation and experiment. The influence of the individual layer thicknesses on the solar cell performance is discussed and approaches for further improvements are given. For the silver film/capping layer electrode, a higher solar cell efficiency could be achieved compared to a reference ZnO:Al front contact

SiNED-Ancillary Services for Reliable Power Grids in Times of Progressive German Energiewende and Digital Transformation

Within SiNED research project, several members of the Energy Research Centre of Lower Saxony (Energieforschungszentrum Niedersachsen, EFZN) are working on various issues relating to the future provision of ancillary services and to future congestion management. The questions include energy technology, economic and energy law aspects as well as information and communications technology (ICT) and data. The investigations are based on Lower Saxony and the framework conditions there. The temporal focus of the investigations is the year 2030