Research on drives and power electronics.

This PCIM contribution validates an ultra-fast, stator-flux-oriented start-up for induction generators in trailer-based mobile refrigeration units, backed by measurement results. A novel energy-based method delivers robust, high-bandwidth DC-link voltage control with very low DC-link capacitance requirements.

Relevance: The work extends the control perspective beyond classical traction drives to mobile energy supply and auxiliary systems. The relevant question is how the system state is optimally controlled with regards to the components stored energy.

The PCIM poster contribution addresses a flux-track-curve-based optimisation constraint for Optimal Pulse Patterns in the PWM context of automotive applications.

Relevance: The work directly matches the core profile of the site: pulse patterns are not treated in isolation, but as a tool for better inverter and machine-current utilisation under practical constraints.

The article presents and studies a novel regression-based neural-network method for power-electronic capacitors for enhanced MMC capacitor condition monitoring with reduced training data requirements.

Relevance: The work is relevant because data-based condition assessment is only industrially useful if it works with limited training data and if it can effectively interpolate system states beyond ones presented to a neural network in its training stage. It adds component monitoring, generalisation, and diagnostics to the profile.

The conference paper transfers stator-flux-oriented induction-generator control to mobile and generator applications in trailer-based refrigeration units. A novel energy-based approach enables highly robust, highly dynamic DC-link voltage control with very small required DC-link capacitance.

Relevance: The work extends the control perspective beyond classical traction drives to mobile energy supply and auxiliary systems. The relevant question is how the system state is optimally controlled with regards to the components stored energy.

The paper uses power-theory quantities as a low-dimensional feature basis to make fault detection and fault identification in power-electronic systems more efficient and interpretable.

Relevance: The work shows how physically motivated signal transformations can reduce the burden on data-driven diagnostic methods. It connects model understanding, system diagnostics, and robust evaluation in power electronics.