Prediction of efficiency-optimized salient-pole synchronous machines' operating range using a coupled numerical-analytical method

Korolova, Olga; Duck, Peter W.; Brune, A.; Jürgens, Jonathan; Ponick, Bernd

doi:10.1109/icelmach.2014.6960283

Cited by 9 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…OFTC of synchronous machine (SM) is currently an actively researched and widely investigated topic: in particular of permanent magnet synchronous machine (PMSM) and reluctance synchronous machine (RSM) (see e.g., [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]) and of electrically excited synchronous machine (EESM; see, e.g, [49][50][51][52][53][54][55][56][57][58]). But only [48] considers and allows for (i) arbitrary nonlinearities (e.g., saturation, (d, q)-crosscoupling and cross-coupling between stator and exciter), (ii) iron and copper losses, (iii) stator and exciter voltage and current limits and (iiii) an analytical computation for a realtime implementation.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Monzen,

Stroebl,

Palm

et al. 2024

IEEE Open J. Ind. Electron. Soc.

View full text Add to dashboard Cite

Multi-objective hyperparameter optimization (MO-HPO) is applied to find optimal artificial neural network (ANN) architectures used for optimal feedforward torque control (OFTC) of synchronous machines. The proposed framework allows to systematically identify Pareto optimal ANNs with respect to multiple (partly) contradictory objectives such as approximation accuracy and computational burden of the considered ANNs. The obtained Pareto optimal ANNs are trained and implemented on a realtime system and tested experimentally for a nonlinear reluctance synchronous machine (RSM) against non-Pareto optimal ANN designs and a state-of-the-art OFTC approach. Finally, based on the most recent results from ANN approximation theory, guidelines for Pareto optimal ANN-based OFTC design and implementation are provided.

show abstract

Section: Introductionmentioning

confidence: 99%

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Monzen,

Stroebl,

Palm

et al. 2024

IEEE Open J. Ind. Electron. Soc.

View full text Add to dashboard Cite

show abstract

“…In [28][29][30] numerical identification and search algorithms are proposed as offline OFTC approaches for EESM that are not suitable for a real-time implementation.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network based optimal feedforward torque control of electrically excited synchronous machines

Monzen¹,

Hackl²

2023

Preprint

View full text Add to dashboard Cite

<p>An Artificial Neural Network (ANN) based Optimal Feedforward Torque Control (OFTC) strategy for electrically excited synchronous machines (EESMs) is proposed. After design, data set creation, training and validation of the ANN, the analytical computation of the optimal stator and exciter currents is achieved which allows to minimize copper and iron losses and to produce the desired (or maximally feasible) machine torque. Voltage and currents constraints of stator and exciter are considered as well. In contrast to conventional OFTC, the proposed ANN-based OFTC strategy does not require iterations nor a decision tree to find the optimal current triple while machine nonlinearities, magnetic cross-coupling, saturation and speed-dependent iron losses are taken into account. In addition, the proposed ANN design procedure allows to consider <em>measurable </em>OFTC goals and computational resources that ensures a real-time capable implementation. Comprehensive simulation results for a real and nonlinear EESM clearly show these benefits by comparing the proposed ANN-based OFTC with results of a nonlinear optimization problem (NLP) solver (which cannot be used in real-time). </p>

show abstract

NVH-Simulation of Salient-Pole Synchronous Machines for Traction Applications

Vip

Hollmann

Ponick

2019

2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) &Amp; 2019 International Conference O

View full text Add to dashboard Cite

Prediction of efficiency-optimized salient-pole synchronous machines' operating range using a coupled numerical-analytical method

Cited by 9 publications

References 6 publications

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Artificial neural network based optimal feedforward torque control of electrically excited synchronous machines

NVH-Simulation of Salient-Pole Synchronous Machines for Traction Applications

Contact Info

Product

Resources

About