Montazeri Najafabadi, Ali (2021)
Efficiency Optimization of Induction Machine Drives with Model Predictive Control.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00017562
Dissertation, Erstveröffentlichung, Verlagsversion
Kurzbeschreibung (Abstract)
This thesis studies a system-oriented and software-based efficiency optimization approach for modern low voltage variable speed drives. For this purpose, a Finite Control Set Model Predictive Control (FCS-MPC) for an inverter-fed Induction Machine (IM) is developed, focusing on novel cost functions and long prediction horizons. Furthermore, a low cost FPGA-based platform is used for real-time hardware implementations.
The conventional cost function, which takes merely the number of switching transitions into account, is redefined in order to explicitly consider individual switching and conduction losses of semiconductors. This is beneficial since the inverter losses can be reduced more effectively. The proposed cost function outperforms the conventional cost function by improving the inverter losses vs. current harmonic distortion (causing IM harmonic conduction losses) trade-off curve. The developed cost function is further enhanced in order to include the IM harmonic magnetization losses as well. The IM harmonic losses are particularly important for system-oriented efficiency optimization since they are in the same order of magnitude as the inverter losses in low voltage applications.
As a distinctive feature, the developed FCS-MPC minimizes the inverter losses as well as the IM harmonic losses in the same cost function. This enables the controller to provide system-oriented global optimum efficiency while keeping its high dynamic performance. Furthermore, the controller can actively and intentionally move the losses between inverter and IM depending on the application and operating point. This is advantageous in order to avoid unacceptable temperatures in either inverter or IM. The benefits of the developed cost function toward efficiency optimization of inverter-fed IM as well as its dynamic performance are evaluated experimentally. The results confirm the superior performance of the developed FCS-MPC compared to standard pulse width modulator-based controllers.
In the final part of this thesis, a framework is developed in order to increase the prediction horizon by means of Dynamic Programming (DP) optimization method. This approach ensures the linear increase of prediction and optimization effort and keeps the computational complexity affordable. For a successful implementation of the algorithm in real time, the resource sharing method for FPGA is discussed in detail. FCS-MPC with long prediction horizons, designed based on dynamic programming approach and resource sharing technique, is evaluated through simulation and experiment.
| Typ des Eintrags: | Dissertation | ||||
|---|---|---|---|---|---|
| Erschienen: | 2021 | ||||
| Autor(en): | Montazeri Najafabadi, Ali | ||||
| Art des Eintrags: | Erstveröffentlichung | ||||
| Titel: | Efficiency Optimization of Induction Machine Drives with Model Predictive Control | ||||
| Sprache: | Englisch | ||||
| Referenten: | Griepentrog, Prof. Dr. Gerd ; Bojoi, Prof. Dr. Radu | ||||
| Publikationsjahr: | 2021 | ||||
| Ort: | Darmstadt | ||||
| Kollation: | xiii, 160 Seiten | ||||
| Datum der mündlichen Prüfung: | 5 November 2020 | ||||
| DOI: | 10.26083/tuprints-00017562 | ||||
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/17562 | ||||
| Kurzbeschreibung (Abstract): | This thesis studies a system-oriented and software-based efficiency optimization approach for modern low voltage variable speed drives. For this purpose, a Finite Control Set Model Predictive Control (FCS-MPC) for an inverter-fed Induction Machine (IM) is developed, focusing on novel cost functions and long prediction horizons. Furthermore, a low cost FPGA-based platform is used for real-time hardware implementations. The conventional cost function, which takes merely the number of switching transitions into account, is redefined in order to explicitly consider individual switching and conduction losses of semiconductors. This is beneficial since the inverter losses can be reduced more effectively. The proposed cost function outperforms the conventional cost function by improving the inverter losses vs. current harmonic distortion (causing IM harmonic conduction losses) trade-off curve. The developed cost function is further enhanced in order to include the IM harmonic magnetization losses as well. The IM harmonic losses are particularly important for system-oriented efficiency optimization since they are in the same order of magnitude as the inverter losses in low voltage applications. As a distinctive feature, the developed FCS-MPC minimizes the inverter losses as well as the IM harmonic losses in the same cost function. This enables the controller to provide system-oriented global optimum efficiency while keeping its high dynamic performance. Furthermore, the controller can actively and intentionally move the losses between inverter and IM depending on the application and operating point. This is advantageous in order to avoid unacceptable temperatures in either inverter or IM. The benefits of the developed cost function toward efficiency optimization of inverter-fed IM as well as its dynamic performance are evaluated experimentally. The results confirm the superior performance of the developed FCS-MPC compared to standard pulse width modulator-based controllers. In the final part of this thesis, a framework is developed in order to increase the prediction horizon by means of Dynamic Programming (DP) optimization method. This approach ensures the linear increase of prediction and optimization effort and keeps the computational complexity affordable. For a successful implementation of the algorithm in real time, the resource sharing method for FPGA is discussed in detail. FCS-MPC with long prediction horizons, designed based on dynamic programming approach and resource sharing technique, is evaluated through simulation and experiment. |
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| Status: | Verlagsversion | ||||
| URN: | urn:nbn:de:tuda-tuprints-175622 | ||||
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau | ||||
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Stromrichtertechnik und Antriebsregelung |
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| Hinterlegungsdatum: | 19 Mär 2021 14:15 | ||||
| Letzte Änderung: | 23 Mär 2021 08:55 | ||||
| PPN: | |||||
| Referenten: | Griepentrog, Prof. Dr. Gerd ; Bojoi, Prof. Dr. Radu | ||||
| Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: | 5 November 2020 | ||||
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| Suche nach Titel in: | TUfind oder in Google | ||||
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