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.

Development of a Range of Modified Multifunctional Residual Current Systems and Their Industrial Implementation

The developments of the AltSTU have been reviewed in the area of creating a new technology for preventing technogenic hazards based on the residual current devices. The residual current devices are intended for protecting people from electric shock in case of contact with conductive parts of the electric appliances and shall facilitate reduction of fire risks caused by a prolonged flow of leakage currents and fault currents resulting from them. The results of creating different modifications of protective trip circuits and their industrial use are provided.