A Parametrization Scheme for High Performance Thermal Models of Electric Machines using Modelica

Haumer, Anton; Kral, Christian; Vuković, Vladimir; David, A.; Hettfleisch, Christian; Huzsvar, Attila

doi:10.3182/20120215-3-at-3016.00187

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…In the implementation of the KF algorithm in NCAP, all the memory blocks are allocated statically so that fragmentation can be avoided [4]. By using it this way, it is easy to analyze the memory (both RAM and Flash) usage.…”

Section: Memory Usage and Calculation Timementioning

confidence: 99%

A8.2 - Temperature Estimation of Induction Machines Based on Wireless Sensor Networks

Huang¹,

Gühmannn²

2017

Proceedings Sensor 2017

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The 4 th -order Kalman filter (KF) algorithm is implemented in the wireless sensor node to estimate the temperatures of the stator winding, the rotor cage and the stator core in the induction machine. Three separate wireless sensor nodes are used to acquire and preprocess the input signals. The hall sensors are used to acquire the three-phase stator currents and voltages of the induction machine. All of them are processed to Root-Mean-Square (RMS) in ampere and volt. A rotary encoder is mounted for the rotor speed and PT1000 is used for the temperature of the coolant air. The processed signals in the physical unit are transmitted wirelessly to the host wireless sensor node, where the KF is implemented with fixed-point arithmetic in Contiki OS. Compared to the floating-point implementation, the fixed-point implementation has the same estimation accuracy of 97% at only about one-fifth of computation time. Temperatures of the machine could be monitored by an App on a smart phone with internet.

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Section: Memory Usage and Calculation Timementioning

confidence: 99%

A8.2 - Temperature Estimation of Induction Machines Based on Wireless Sensor Networks

Huang¹,

Gühmannn²

2017

Proceedings Sensor 2017

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“…All these losses are dissipated either to an internal thermal ambient with fixed operational temperature, or exchanged with an external thermal model through the thermal port. An external thermal model allows to simulate the actual temperatures over a driving cycle as discussed, e.g., in [6,7].…”

Section: Machines and Sensorsmentioning

confidence: 99%

The New EDrives Library: A Modular Tool for Engineering of Electric Drives

Haumer

Kral²

2014

Linköping Electronic Conference Proceedings

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Simulation is an indispensable tool for the engineering of systems containing electric drives. Depending on the design phase and the engineering task different levels of modeling details are required: proof of concept, investigation of energy and power consumption, design of control, etc. The new EDrives library provides three levels of abstraction for inverters: quasi static (neglecting electrical transients), averaging (neglecting switching effects) and switching-for serving different demands. The inverters can feed the machine models of the Modelica Standard Library: Modelica.Magnetic.FundamentalWave and the new Modelica.Magnetic.QuasiStatic.FundamentalWave. The EDrives library copes with arbitrary phase numbers and can be easily extended to develop new control algorithms. In this publication the structure of the library and the implemented control principles are presented. Furthermore, examples comparing the three different levels of abstraction are included.

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“…In the implementation of the KF algorithm in the NCAP, all the memory blocks are allocated statically so that fragmentation can be avoided (Haumer et al, 2012). By using this way, it is easy to analyze the memory usage of both RAM and Flash.…”

Section: Memory Usage and Calculation Timementioning

confidence: 99%

“…Based on the stator windings resistance variation with temperature, a sensorless internal temperature monitoring method for an induction motor is introduced (Sabaghi et al, 2007). Thermal analysis based on a lumped-parameter thermal network (Haumer et al, 2012) is a third way which can be used for the temperature monitoring directly.…”

Section: Introductionmentioning

confidence: 99%

Temperature estimation of induction machines based on wireless sensor networks

Huang

Gühmann

2018

J. Sens. Sens. Syst.

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Abstract. In this paper, a fourth-order Kalman filter (KF) algorithm is implemented in the wireless sensor node to estimate the temperatures of the stator winding, the rotor cage and the stator core in the induction machine. Three separate wireless sensor nodes are used as the data acquisition systems for different input signals. Six Hall sensors are used to acquire the three-phase stator currents and voltages of the induction machine. All of them are processed to root mean square (rms) in ampere and volt. A rotary encoder is mounted for the rotor speed and Pt-1000 is used for the temperature of the coolant air. The processed signals in the physical unit are transmitted wirelessly to the host wireless sensor node, where the KF is implemented with fixed-point arithmetic in Contiki OS. Time-division multiple access (TDMA) is used to make the wireless transmission more stable. Compared to the floating-point implementation, the fixed-point implementation has the same estimation accuracy at only about one-fifth of the computation time. The temperature estimation system can work under any work condition as long as there are currents through the machine. It can also be rebooted for estimation even when wireless transmission has collapsed or packages are missing.

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A Parametrization Scheme for High Performance Thermal Models of Electric Machines using Modelica

Cited by 4 publications

References 2 publications

A8.2 - Temperature Estimation of Induction Machines Based on Wireless Sensor Networks

A8.2 - Temperature Estimation of Induction Machines Based on Wireless Sensor Networks

The New EDrives Library: A Modular Tool for Engineering of Electric Drives

Temperature estimation of induction machines based on wireless sensor networks

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