Chris Gerada

TL;DR: In this article , a detailed investigation of losses and machine performance, taking into account different operating conditions, is presented, where 3D FE models were used to accurately predict eddy current losses in the rotor components.

TL;DR: A surrogate approach which utilises an supervised neural network to significantly shorten the time required for thermal qualification of electrical machines’ insulation using a feedforward neural network trained with Bayesian Regularization Back-Propagation (BRP) algorithm.

TL;DR: In this article, a 3D modeling based on finite-element analysis directly coupled with a power converter control system is built in order to accurately evaluate generator performance, including the influence of the control system current time harmonics on the 3D flux distribution, the vector eddy current in rotor sleeve, and the iron loss of the machine.

TL;DR: In this paper, the authors present a Power Electronic Inverter library in Modelica, which uses a multi-level approach with increasing model complexity at progressively higher levels and includes losses between the input and output of the power electronic inverter irrespective of the direction of power flow.

TL;DR: The focus of the paper is on the power sharing transient controllability achieved by using a sharing regulator based on the droop controller, which was introduced for the first time by Fingas and Lehn.