Reinforcement Learning in Micro- and Smart Grids: Safe, Data-Driven Operating Strategies for Complex Energy Systems

Reinforcement Learning in Micro- and Smart Grids: Safe, Data-Driven Operating Strategies for Complex Energy Systems

The research project is funded and supported by the Committee for Research and Junior Academics at Paderborn University. It was awarded the research prize of the university in 2019.

Project acronym: RLMG
Funding period: 1 year (2020/01/01 – 2020/12/31)
Project partner: Department of Intelligent Systems and Machine Learning
Project profile: https://www.upb.de/en/research/fk/research-award-winners/

Motivation

The transformation of the current energy system to a sustainable structure characterized by renewable energies is a central social challenge of the 21st century. To achieve this, the inherent volatility of renewable energy sources requires a move away from conventional, hierarchically structured top-down energy networks towards flexible, cross-sectoral and intelligent energy systems. Therefore, in the course of the energy transition, so-called micro- and smart grids (MSG) represent an important solution component to ensure a clean, efficient and cost-effective energy supply in the future. MSG is the concept of a local grid consisting of energy sources (e.g. wind power), energy storage (e.g. battery) and energy consumers of different sectors (e.g. electricity, heat, mobility). The local integration of regenerative energies by means of MSGs, e.g. within industrial companies or residential areas, relieves the energy networks and, thus, reduces the need for cost- and resource-intensive network expansion.

The central hurdle to the establishment of MSGs is safe operation. MSGs are highly heterogeneous, complex and have a significant stochastic component, which is caused by the uncertainty of consumer behavior and renewable power plants. Classical methods of control engineering are not considered to be effective here. In contrast, reinforcement learning (RL) is a data-driven operating concept from the field of machine learning (ML), which has been able to celebrate promising success with similarly complex and stochastic problems (e.g. stock exchange trading). Nevertheless, research into MSG-specific ML strategies is still fraught with a high risk, since the security and availability of energy networks must meet the highest requirements: even a single wrong decision can lead to a complete system failure (black-out). In the absence of mathematically provable guarantees, the use of adaptive, data-driven methods of ML, whose behavior is fundamentally unpredictable, is extremely challenging in this context.

Project Objectives

The project should answer the following questions: Are RL-based operation strategies in principle able to control complex, heterogeneous and stochastic MSGs under highest security and availability requirements? Which MSG-specific methodological extensions are to be developed for reinforcement learning in order to achieve this goal?

There are basically two possible variants for operating MSGs. In the centralized approach, an agent (controller) derives control commands for all components in the MSG (e.g. battery storage) based on information about the entire system. In contrast, in the distributed concept, each entity decides on its own operating behavior (multi-agent system, MAS). This approach tends to be more robust and reduces the risk of a total failure as it is not dependent on a central component. However, the large number of decision makers increases system complexity, which increases the risk of wrong decisions in MAS as a whole.

In the course of the project, both approaches are to be examined with regard to their suitability, since no statements can yet be made on the basis of the available literature on related tasks. In order to achieve the primary goal of safe MSG operation, existing RL methods have to be extended in such a way that the learned models meet certain boundary conditions and fulfil given guarantees. In the case of decentralized control, this means the development of safe methods of MAS-RL, a very difficult problem for which there are no established approaches in the literature so far. In addition, further objectives are to be included in the investigation, e.g. minimizing operating and investment costs or maximizing regenerative energy to minimize environmental pollution. This goes hand in hand with the extension of RL methods to the case of multi-criteria optimization, for which no viable solution concepts exist so far either.

For numerical investigations a scalable model environment representing different MSG topologies is created. The goal is to automatically design arbitrary MSGs in order to evaluate the RL strategies under investigation with respect to their practicability and generalizability. In the long term, a transfer to the real test environment Microgrid-Laboratory, which is currently under construction in the LEA department, is planned.

Contact:

Dr.-Ing. Oliver Wallscheid

Kontakt

Publikationen

Leistungselektronik und Elektrische Antriebstechnik (LEA)

Oberingenieur - Mitarbeiter - Leitung Fachgruppe Elektrische Antriebstechnik & Intelligente Energiesysteme

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+49 5251 60-3653

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+49 5251 60-3443

E-Mail:

wallscheid(at)lea.upb(dot)de

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E4.124

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Weitere Arbeitsbereiche

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2023

Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning

S. Peitz, J. Stenner, V. Chidananda, O. Wallscheid, S.L. Brunton, K. Taira, in: arXiv:2301.10737, 2023

Abstract

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arXiv

We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational invariances, the high-dimensional distributed control problem can be transformed into a multi-agent control problem with many identical, uncoupled agents. Furthermore, using the fact that information is transported with finite velocity in many cases, the dimension of the agents' environment can be drastically reduced using a convolution operation over the state space of the PDE. In this setting, the complexity can be flexibly adjusted via the kernel width or by using a stride greater than one. Moreover, scaling from smaller to larger systems -- or the transfer between different domains -- becomes a straightforward task requiring little effort. We demonstrate the performance of the proposed framework using several PDE examples with increasing complexity, where stabilization is achieved by training a low-dimensional deep deterministic policy gradient agent using minimal computing resources.

https://arxiv.org/pdf/2301.10737

Time-Optimal Model Predictive Control of Permanent Magnet Synchronous Motors Considering Current and Torque Constraints

A. Brosch, O. Wallscheid, J. Böcker, IEEE Transactions on Power Electronics (2023), pp. 1-14

DOI

2022

An Open-Source Transistor Database and Toolbox as an Unified Software Engineering Tool for Managing and Evaluating Power Transistors

N. Förster, P. Rehlaender, O. Wallscheid, F. Schafmeister, J. Böcker, in: Proc. 37th IEEE Applied Power Electronics Conference (APEC), IEEE, 2022

Model Predictive Torque Control for Permanent Magnet Synchronous Motors Using a Stator-Fixed Harmonic Flux Reference Generator in the Entire Modulation Range

A. Brosch, O. Wallscheid, J. Böcker, IEEE Transactions on Power Electronics (2022)

DOI

Long-Term Memory Recursive Least Squares Online Identification of Highly Utilized Permanent Magnet Synchronous Motors for Finite-Control-Set Model Predictive Control

A. Brosch, O. Wallscheid, J. Böcker, IEEE Transactions on Power Electronics (2022)

DOI

Experimental Comparison of FPGA-Implemented Model Predictive Voltage Control to Cascaded Proportional Resonant Control for a Three-Phase Four-Wire Three-Level Grid-Forming Inverter of 250 kVA

J. Lange, D. Schmies, K.S.C. Stille, J. Böcker, O. Wallscheid, in: EPE'22 ECCE Europe, IEEE, 2022

Adaptive Operating Strategy for Induction Motors Under Changing Electrical-Thermal Conditions

M. Stender, M. Becker, O. Wallscheid, J. Böcker, in: 48th Annual Conference of the Industrial Electronics Society (IECON), 2022

DOI

An Open-Source Transistor Database and Toolbox as a Unified Software Engineering Tool for Managing and Evaluating Power Transistors

N. Förster, P. Rehlaender, O. Wallscheid, F. Schafmeister, J. Böcker, in: 2022 IEEE Applied Power Electronics Conference and Exposition (APEC), IEEE, 2022

DOI

An Open-Source FEM Magnetics Toolbox for Power Electronic Magnetic Components

N. Förster, T. Piepenbrock, P. Rehlaender, O. Wallscheid, F. Schafmeister, J. Böcker, in: PCIM Europe 2022; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, 2022, pp. 1-10

DOI

Reinforcement Learning-Based Deep Q Direct Torque Control with Adaptable Switching Frequency Towards Six-Step Operation of Permanent Magnet Synchronous Motors

B. Haucke-Korber, M. Schenke, O. Wallscheid, in: IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp. 1-6

Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning

W. Kirchgässner, O. Wallscheid, J. Böcker, Engineering Applications of Artificial Intelligence (2022), 117, 105537

DOI

Learning Thermal Properties and Temperature Models of Electric Motors with Neural Ordinary Differential Equations

W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), IEEE, 2022

DOI

LLC Resonant Converter Modulations for Reduced Junction Temperatures in Half-Bridge Mode and Transformer Flux in the On-the-Fly Morphing Thereto

P. Rehlaender, O. Wallscheid, F. Schafmeister, J. Böcker, IEEE Transactions on Power Electronics (2022), 37(11), pp. 13413-13427

DOI

Application of Thermal Neural Networks on a Small-Scale Electric Motor

W. Kirchgässner, D. Wöckinger, O. Wallscheid, G. Bramerdorfer, J. Böcker, in: IKMT 2022; 13. GMM/ETG-Symposium, 2022, pp. 1-6

Data-Driven Adaptive Torque Oscillation Compensation for Multi-Motor Drive Systems

A. Brosch, J. Rauhaus, O. Wallscheid, J. Böcker, D. Zimmer, IEEE Open Journal of Industry Applications (2022)

DOI

2021

Model Predictive Control of Permanent Magnet Synchronous Motors in the Overmodulation Region Including Six-Step Operation

A. Brosch, O. Wallscheid, J. Böcker, IEEE Open Journal of Industry Applications (2021), 2, pp. 47–63

DOI

Temperature estimation of electric machines using a hybrid model of feed-forward neural and low-order lumped-parameter thermal networks

E.G. Gedlu, O. Wallscheid, J. Böcker, in: 2021 IEEE International Electric Machines & Drives Conference (IEMDC), 2021, pp. 1–8

DOI

Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments

G. Book, A. Traue, P. Balakrishna, A. Brosch, M. Schenke, S. Hanke, W. Kirchgässner, O. Wallscheid, IEEE Open Journal of Power Electronics (2021), pp. 187-201

DOI

Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments

D. Weber, S. Heid, H. Bode, J. Lange, E. Hüllermeier, O. Wallscheid, IEEE Access (2021), 9, pp. 35654–35669

DOI

Accurate Torque Control for Induction Motors by Utilizing a Globally Optimized Flux Observer

M. Stender, O. Wallscheid, J. Böcker, IEEE Transactions on Power Electronics (2021), 36(11), pp. 13261-13274

DOI

Accurate Torque Estimation for Induction Motors by Utilizing a Hybrid Machine Learning Approach

M. Stender, O. Wallscheid, J. Böcker, in: 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC), IEEE, 2021

DOI

Combined Electrical-Thermal Gray-Box Model and Parameter Identification of an Induction Motor

M. Stender, O. Wallscheid, J. Böcker, in: IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2021

DOI

Gray-Box Loss Model for Induction Motor Drives

M. Stender, O. Wallscheid, J. Böcker, in: 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC), IEEE, 2021

DOI

Comparison of Artificial Neural Network and Least Squares Prediction Models for Finite-Control-Set Model Predictive Control of a Permanent Magnet Synchronous Motor

S. Hanke, O. Wallscheid, J. Böcker, IET Digital Library, 2021

DOI

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

O. Wallscheid, IEEE Open Journal of Industry Applications (2021)

Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning: A Benchmark

W. Kirchgässner, O. Wallscheid, J. Böcker, IEEE Transactions on Energy Conversion (2021), 36(3), pp. 2059 - 2067

DOI

gym-electric-motor (GEM): A Python toolbox for the simulation of electric drive systems

P. Balakrishna, G. Book, W. Kirchgässner, M. Schenke, A. Traue, O. Wallscheid, Journal of Open Source Software (2021), 2498

DOI

A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors

M. Schenke, O. Wallscheid, IEEE Open Journal of the Industrial Electronics Society (2021), pp. 388-400

DOI

Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning

M. Schenke, O. Wallscheid, arXiv preprint arXiv:2105.08990 (2021)

Torque and Inductances Estimation for Finite Model Predictive Control of Highly Utilized Permanent Magnet Synchronous Motors

A. Brosch, O. Wallscheid, J. Böcker, IEEE Transactions on Industrial Informatics (2021)

DOI

Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning

W. Kirchgässner, O. Wallscheid, J. Böcker, in: arXiv preprint arXiv:2103.16323, 2021

2020

Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control

A. Traue, G. Book, W. Kirchgässner, O. Wallscheid, IEEE Transactions on Neural Networks and Learning Systems (2020), pp. 1-10

DOI

Estimating Electric Motor Temperatures with Deep Residual Machine Learning

W. Kirchgässner, O. Wallscheid, J. Böcker, IEEE Transactions on Power Electronics (2020), 36(7), pp. 7480-7488

DOI

Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors

A. Brosch, S. Hanke, O. Wallscheid, J. Böcker, IEEE Transactions on Power Electronics (2020), pp. 2179-2190

DOI

Emulation of Microgrids for Research and Validation of Control and Operation Strategies

K.S.C. Stille, D. Weber, J. Lange, T. Vogt, O. Wallscheid, J. Böcker, in: 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), IEEE, 2020

DOI

Comparison of Gray-Box and Black-Box Two-Level Three-Phase Inverter Models for Electrical Drives

M. Stender, O. Wallscheid, J. Böcker, IEEE Transactions on Industrial Electronics (2020), 68(9), pp. 8646-8656

DOI

Accurate Torque Estimation for Induction Motors by Utilizing Globally Optimized Flux Observers

M. Stender, O. Wallscheid, J. Böcker, in: 2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), IEEE, 2020

DOI

OMG: A scalable and flexible simulation and testing environment toolbox for intelligent microgrid control

S. Heid, D. Weber, H. Bode, E. Hüllermeier, O. Wallscheid, Journal of Open Source Software (2020), 5(54), pp. 2435

Towards a scalable and flexible simulation and testing environment toolbox for intelligent microgrid control

H. Bode, S. Heid, D. Weber, E. Hüllermeier, O. Wallscheid, arXiv preprint arXiv:2005.04869 (2020)

Permanent magnet synchronous machine temperature estimation using low-order lumped-parameter thermal network with extended iron loss model

E.G. Gedlu, O. Wallscheid, J. Böcker, in: The 10th International Conference on Power Electronics, Machines and Drives (PEMD 2020), 2020, pp. 937–942

Investigation of disturbance observers for model predictive current control in electric drives

O. Wallscheid, E.F.B. Ngoumtsa, IEEE Transactions on Power Electronics (2020), 35(12), pp. 13563–13572

Data Set Description: Identifying the Physics Behind an Electric Motor–Data-Driven Learning of the Electrical Behavior (Part II)

S. Hanke, O. Wallscheid, J. Böcker, arXiv preprint arXiv:2003.06268 (2020)

Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning

W. Kirchgässner, O. Wallscheid, J. Böcker, arXiv preprint arXiv:2001.06246 (2020)

Data Set Description: Three-Phase IGBT Two-Level Inverter for Electrical Drives

M. Stender, O. Wallscheid, J. Böcker, 2020

DOI

2019

Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept

M. Schenke, W. Kirchgässner, O. Wallscheid, IEEE Transactions on Industrial Informatics (2019), pp. 4650-4658

DOI

Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines

W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), 2019

DOI

Deep Residual Convolutional and Recurrent Neural Networks for Temperature Estimation in Permanent Magnet Synchronous Motors

W. Kirchgässner, O. Wallscheid, J. Böcker, in: 2019 IEEE International Electric Machines & Drives Conference (IEMDC), 2019

DOI

Development of a Black-Box Two-Level IGBT Three-Phase Inverter Compensation Scheme for Electrical Drives

M. Stender, O. Wallscheid, J. Böcker, in: 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), IEEE, 2019

DOI

Finite-control-set model predictive control for a permanent magnet synchronous motor application with online least squares system identification

S. Hanke, S. Peitz, O. Wallscheid, J. Böcker, M. Dellnitz, in: 2019 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2019, pp. 1–6

Stator flux-based field-oriented position-sensorless control of permanent magnet synchronous motors with limited parameter knowledge

O. Wallscheid, M.S. Shafiq, J. Böcker, in: 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), 2019, pp. 402–407

Hierarchical model predictive speed and current control of an induction machine drive with moving-horizon load torque estimator

O. Wallscheid, E.F.B. Ngoumtsa, J. Böcker, in: 2019 IEEE International Electric Machines & Drives Conference (IEMDC), 2019, pp. 2188–2195

Continuous-control-set model predictive control with integrated modulator in permanent magnet synchronous motor applications

S. Hanke, O. Wallscheid, J. Böcker, in: 2019 IEEE International Electric Machines & Drives Conference (IEMDC), 2019, pp. 2210–2216

Improved Fusion of Permanent Magnet Temperature Estimation Techniques for Synchronous Motors Using a Kalman Filter

D.E.E. Gaona, O. Wallscheid, J. Böcker, IEEE Transactions on Industrial Electronics (2019)

2018

Energy Management for a Nano-CHP Unit and an Electrical Storage System in a Residential Application

D. Weber, K.S.C. Stille, O. Wallscheid, J. Böcker, in: 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), IEEE, 2018

DOI

Lifetime Extension of Photovoltaic Modules by Influencing the Module Temperature Using Phase Change Material

D. Weber, M.I. Rafsan Jani, M. Grabo, O. Wallscheid, T. Klaus, S. Krauter, J. Böcker, in: World Conference on Photovoltaic Energy Conversion (WCPEC-7), 45th IEEE PVSC, 28th PVSEC, 34th EU PVSEC., 2018

DOI

Improving torque and speed estimation accuracy by conjoint parameter identification and unscented Kalman filter design for induction machines

O. Wallscheid, M. Schenke, J. Böcker, in: 2018 21st International Conference on Electrical Machines and Systems (ICEMS), 2018, pp. 1181–1186

A combined approach to identify induction machine parameters and to design an extended kalman filter for speed and torque estimation

O. Wallscheid, M. Schenke, J. Böcker, in: 2018 IEEE 18th International Power Electronics and Motion Control Conference (PEMC), 2018, pp. 793–799

Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives

S. Hanke, S. Peitz, O. Wallscheid, S. Klus, J. Böcker, M. Dellnitz, arXiv preprint arXiv:1804.00854 (2018)

2017

Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors

O. Wallscheid, W. Kirchgässner, J. Böcker, in: 2017 International Joint Conference on Neural Networks (IJCNN), 2017

DOI

Glocal identification methods for low-order lumped parameter thermal networks used in permanent magnet synchronous motors

D. Gaona, O. Wallscheid, J. Böcker, in: 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS), 2017, pp. 1–126

Sensitivity analysis of a permanent magnet temperature observer for PM synchronous machines using the monte carlo method

D. Gaona, O. Wallscheid, J. Böcker, in: 2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS), 2017, pp. 599–606

A direct model predictive torque control approach to meet torque and loss objectives simultaneously in permanent magnet synchronous motor applications

S. Hanke, O. Wallscheid, J. Böcker, in: 2017 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2017, pp. 101–106

Observing the Permanent Magnet Temperature of Synchronous Motors Based on Electrical Fundamental Wave Model Quantities

O. Wallscheid, A. Specht, J. Böcker, IEEE Transactions on Industrial Electronics (2017), 64(5), pp. 3921–3929

Fusion of a lumped-parameter thermal network and speed-dependent flux observer for PM temperature estimation in synchronous machines

D. Gaona, O. Wallscheid, J. Böcker, in: 2017 IEEE Southern Power Electronics Conference (SPEC), 2017, pp. 1–6

Fusion of direct and indirect temperature estimation techniques for permanent magnet synchronous motors

O. Wallscheid, J. Böcker, in: 2017 IEEE International Electric Machines and Drives Conference (IEMDC), 2017, pp. 1–8

Derating of automotive drive systems using model predictive control

O. Wallscheid, J. Böcker, in: 2017 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2017, pp. 31–36

Prediction of residual power peaks in industrial microgrids using artificial neural networks

T. Vogt, D. Weber, O. Wallscheid, J. Böcker, in: International Joint Conference on Neural Networks (IJCNN), 2017, pp. 3228–3235

Ein Beitrag zur thermischen Ausnutzung permanenterregter Synchronmotoren in automobilen Traktionsanwendungen

O. Wallscheid, Shaker Verlag, 2017

Abstract

Essentielle Anforderungen an Antriebssysteme für (teil-)elektrisch angetriebene Fahrzeuge sind eine hohe Leistungs- und Drehmomentdichte. Hierbei determinieren insbesondere die zulässigen Temperaturen wichtiger Motorkomponenten das transient sowie dauerhaft erzielbare Leistungspotential. Für die in automobilen Anwendungen häufig eingesetzten permanenterregten Synchronmotoren ist die Temperaturverteilung innerhalb der Wicklung und der Permanentmagnete von besonderem Interesse. Um den thermischen Bauteilschutz zu gewährleisten, werden Motoren in der industriellen Praxis häufig überdimensioniert, was zusätzlichen Gewichts- und Bauraumbedarf sowie höhere Produktionskosten bedingt. Alternativ kann ein Derating eingesetzt werden, welches die zulässige Motorleistung in Abhängigkeit des thermischen Zustands regelt. Dies erlaubt, sowohl die thermischen Kapazitäten des Motors gezielt auszunutzen als auch die Leistungsaufnahme im thermisch stationären Betrieb zu maximieren. Substantielle Voraussetzung hierfür ist die Kenntnis wichtiger Temperaturen zur Laufzeit - eine vollständige messtechnische Erfassung dieser ist allerdings aus Kosten- und Ausfallsicherheitsgründen nicht möglich. Im Zuge dieser Arbeit wurden daher sowohl thermische Netzwerke mit konzentrierten Parametern als auch Beobachteransätze auf Basis des elektrischen Motormodells untersucht. Darauf aufbauend wurde eine virtuelle Sensorfusion mittels Kalman-Filter realisiert. Umfangreiche Kreuz-Validierungen belegen eine hochgenaue, echtzeitfähige Temperaturermittlung mit einer Schätzabweichung von kleiner 5 K. Abschließend wurde ein modellprädiktives Derating-Konzept erarbeitet, welches sowohl die maximale thermische Ausnutzung des Antriebs erlaubt als auch definierte Grenztemperaturen sicher einhält.

2016

A Critical Review of Techniques to Determine the Magnet Temperature of Permanent Magnet Synchronous Motors under Real-time Conditions

O. Wallscheid, T. Huber, W. Peters, J. Böcker, EPE Journal (2016)(25), pp. 1–10

Real-time capable model predictive control of permanent magnet synchronous motors using particle swarm optimisation

O. Wallscheid, U. Ammann, J. Böcker, in: PCIM Europe 2016; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, 2016, pp. 1–8

2015

Global Identification of a Low-Order Lumped-Parameter Thermal Network for Permanent Magnet Synchronous Motors

O. Wallscheid, J. Böcker, IEEE Transactions on Energy Conversion (2015), 31(1), pp. 354–365

An open-loop operation strategy for induction motors considering iron losses and saturation effects in automotive applications

O. Wallscheid, M. Meyer, J. Böcker, in: 2015 IEEE 11th International Conference on Power Electronics and Drive Systems, 2015, pp. 981–985

Design and Identification of a Lumped-Parameter Thermal Network for Permanent Magnet Synchronous Motors Based on Heat Transfer Theory and Particle Swarm Optimisation

O. Wallscheid, J. Böcker, in: European Conference on Power Electronics and Applications (EPE), 2015

Design and Empirical Identification of a Lumped Parameter Thermal Network for Permanent Magnet Synchronous Motors with Physically Motivated Constraints

O. Wallscheid, J. Böcker, in: IEEE International Electric Machines and Drives Conference (IEMDC), 2015

Optimum Efficiency Control of Interior Permanent Magnet Synchronous Motors in Drive Trains of Electric and Hybrid Vehicles

W. Peters, O. Wallscheid, J. Böcker, in: European Conference on Power Electronics and Applications (EPE), 2015

2014

Real-time capable methods to determine the magnet temperature of permanent magnet synchronous motors—A review

O. Wallscheid, T. Huber, W. Peters, J. Böcker, in: IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society, 2014, pp. 811–818

Determination of rotor temperature for an interior permanent magnet synchronous machine using a precise flux observer

A. Specht, O. Wallscheid, J. Böcker, in: 2014 International Power Electronics Conference (IPEC-Hiroshima 2014-ECCE ASIA), 2014, pp. 1501–1507

2013

Wirkungsgradoptimale Arbeitspunktsteuerung für einen permanenterregten Synchronmotor mit vergrabenen Magneten unter Berücksichtigung von Temperatureinflüssen

O. Wallscheid, J. Böcker, in: ETG-Fachbericht-Internationaler ETG-Kongress 2013–Energieversorgung auf dem Weg nach 2050, 2013

Discrete-time model of an IPMSM based on variational integrators

A. Specht, S. Ober-Blöbaum, O. Wallscheid, C. Romaus, J. Böcker, in: Electric Machines & Drives Conference (IEMDC), 2013 IEEE International, 2013, pp. 1411–1417

2012

A precise open-loop torque control for an interior permanent magnet synchronous motor (IPMSM) considering iron losses

W. Peters, O. Wallscheid, J. Böcker, in: IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, 2012, pp. 2877–2882

2011

Optimierte Regelung für mobile Elektromotoren

O. Wallscheid, W. Peters, J. Böcker, Aktuelle Technik (2011), 5(1), pp. 30–33

Current controller with defined dynamic behavior for an interior permanent magnet synchronous motor

W. Peters, O. Wallscheid, J. Böcker, in: IECON 2011-37th Annual Conference of the IEEE Industrial Electronics Society, 2011, pp. 534–538

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