Temperature estimation of induction machines based on wireless sensor networks
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-04-01
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| Series: | Journal of Sensors and Sensor Systems |
| Online Access: | https://www.j-sens-sens-syst.net/7/267/2018/jsss-7-267-2018.pdf |
| _version_ | 1818024225857863680 |
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| author | Y. Huang C. Gühmann |
| author_facet | Y. Huang C. Gühmann |
| author_sort | Y. Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T03:56:50Z |
| format | Article |
| id | doaj.art-5032d8f9a571404c821878c1458a4112 |
| institution | Directory Open Access Journal |
| issn | 2194-8771 2194-878X |
| language | English |
| last_indexed | 2024-12-10T03:56:50Z |
| publishDate | 2018-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Journal of Sensors and Sensor Systems |
| spelling | doaj.art-5032d8f9a571404c821878c1458a41122022-12-22T02:03:05ZengCopernicus PublicationsJournal of Sensors and Sensor Systems2194-87712194-878X2018-04-01726728010.5194/jsss-7-267-2018Temperature estimation of induction machines based on wireless sensor networksY. Huang0C. Gühmann1TU Berlin, Chair of Electronic Measurement and Diagnostic Technology, Sekr. EN13, Einsteinufer 17, 10589 Berlin, GermanyTU Berlin, Chair of Electronic Measurement and Diagnostic Technology, Sekr. EN13, Einsteinufer 17, 10589 Berlin, GermanyIn 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.https://www.j-sens-sens-syst.net/7/267/2018/jsss-7-267-2018.pdf |
| spellingShingle | Y. Huang C. Gühmann Temperature estimation of induction machines based on wireless sensor networks Journal of Sensors and Sensor Systems |
| title | Temperature estimation of induction machines based on wireless sensor networks |
| title_full | Temperature estimation of induction machines based on wireless sensor networks |
| title_fullStr | Temperature estimation of induction machines based on wireless sensor networks |
| title_full_unstemmed | Temperature estimation of induction machines based on wireless sensor networks |
| title_short | Temperature estimation of induction machines based on wireless sensor networks |
| title_sort | temperature estimation of induction machines based on wireless sensor networks |
| url | https://www.j-sens-sens-syst.net/7/267/2018/jsss-7-267-2018.pdf |
| work_keys_str_mv | AT yhuang temperatureestimationofinductionmachinesbasedonwirelesssensornetworks AT cguhmann temperatureestimationofinductionmachinesbasedonwirelesssensornetworks |