Identyfikatory
Warianty tytułu
Particle swarm optimization of an inductionmotor Luenberger observer
Języki publikacji
Abstrakty
W artykule opisano eksperymenty z doborem wzmocnień obserwatora zmiennych stanu silnika indukcyjnego przy wykorzystaniu metody optymalizacyjnej opartej na roju cząstek (PSO). W badaniach skupiono się na porównaniu różnych wersji algorytmu PSO, poddając optymalizacji funkcję celu o zawsze takich samych parametrach. Przeanalizowano trzy różne metody uczenia, GB (Global Best), LB (Local Best) oraz FIPS (Fully Informed Particle Swarm). Dwie ostatnie metody działają w oparciu o zadaną topologię roju, do wyboru spośród kraty pierścieniowej, kraty Von Neumanna oraz FDR (Fitness Distance Ratio). Przeanalizowano zagadnienia zbieżności i stabilności algorytmu, zależne od parametrów takich jak współczynnik uczenia.
The paper describes experiments with the gain selection of an induction motor state observer, using particle swarm optimization (PSO) method. The research focused on comparing different versions of the PSO algorithm, optimizing the fitness function elaborated during preceding research. Three different learning methods were analyzed, GB (Global Best), LB (Local Best) and FIPS (Fully Informed Particle Swarm). The last two methods operate on the basis of a given swarm topology, to be selected from a ring lattice, Von Neumann lattice and FDR (Fitness Distance Ratio). The problems of convergence and stability of the algorithm, depending on parameters such as a cognition factor, were analyzed. The results for 1000 runs of PSO with 20 different sets of parameters were presented and compared.
Wydawca
Czasopismo
Rocznik
Tom
Strony
277--283
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
- Politechnika Śląska, Wydział Elektryczny, Katedra Elektrotechniki i Informatyki, ul. Akademicka 10, 44-100 Gliwice
Bibliografia
- [1] Laatra Y., Lotfi H., Abdelhani B., Speed Sensorless Vector Control of Induction Machine with Luenberger observer and Kalman Filter, Proceedings of International Conference on Control, Decision and Information Technologies (CoDIT'17), (2017), 5–7
- [2] Wang F., Zhang Z., Mei X., Rodríguez J., Kennel R., Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control, Energies, 11 (2018), No. 1:120, 1-13
- [3] Kuchar M., Palacky P., Simonik P., Strossa J., Self-Tuning Observer for Sensor Fault-Tolerant Control of Induction Motor Drive, Energies, 14 (2021), No. 9:2564, 1-16
- [4] Toumi D., Segueir Boucherit M., Tadjine M., Observer-based fault diagnosis and field oriented fault tolerant control of induction motor with stator inter-turn fault, Arch. Electrical Eng., 61 (2012), No. 2, 165-188
- [5] Kubota H., Matsuse K., Nakano T., DSP-based speed adaptive flux observer of induction motor, IEEE Trans. Ind. Appl., 29 (1993), No. 2, 344-348
- [6] Białoń T., Pasko M., Niestrój R., Developing Induction Motor State Observers with Increased Robustness, Energies, 13 (2020), No. 20:5487, 1-24
- [7] Azzoug V., Menacer A., Pusca R., Romary R., Ameid T., Ammar A., Fault Tolerant Control for Speed Sensor Failure in Induc-tion Motor Drive based on Direct Torque Control and Adaptive Stator Flux Observer, Proceedings of International Conference on Applied and Theoretical Electricity (ICATE), (2018), 1-6
- [8] Zaky M.S., Khater M., Yasin H., Shokralla, S.S., Review of different speed estimation schemes for sensorless induction motor drives, JEE, 8 (2017), 102-140
- [9] Kadrine A., Tir Z., Malik O.P., Hamida M.A., Reatti A., Houari A., Adaptive non-linear high gain observer based sensor-less speed estimation of an induction motor, J. Franklin Inst., 357 (2020), 8995-9024
- [10] Najafabadi T. A., Salmasi F. R., Jabehdar-Maralani P., Detection and Isolation of Speed-, DC-Link Voltage-, and Current-Sensor Faults Based on an Adaptive Observer in Induction-Motor Drives, IEEE Trans. Ind. El., 58 (2011), No. 5, 1662-1672
- [11] Pimkumwong N., Wang M.S., Full-order observer for direct torque control of induction motor based on constant V/F control technique, ISA Trans., 73 (2018), 189-200
- [12] Hussein A.A., Salih S.S., Ghasm Y.G., Implementation of Proportional-Integral-Observer Techniques for Load Frequency Control of Power System, Proceedings of International Conference on Ambient Systems, Networks and Technologies, ANT-2017 and the 7th International Conference on Sustainable Energy Information Technology, SEIT 2017, (2017), 754-762
- [13] Białoń T., Niestrój R., Michalak J., Pasko M., Induction Motor PI Observer with Reduced-Order Integrating Unit, Energies, 14 (2021), No. 16:4906, 1-12
- [14] Białoń T., Pasko M., Niestrój R., Developing Induction Motor State Observers with Increased Robustness, Energies, 13 (2020), No. 20:5487, 1-24
- [15] Krzemiński Z., Lewicki A., Morawiec M., Speed observer based on extended model of induction machine, Proceedings of IEEE International Symposium on Industrial Electronics, (2010) 3017-3112
- [16] Białoń T., Lewicki A., Pasko M., Niestrój R., Non-proportional full-order Luenberger observers of induction motors, Arch. Electrical Eng, 67 (2018), 925-937
- [17] Poli R.,ꞏKennedy J.,ꞏBlackwell T., Particle swarm optimization: An overview, Swarm Intell, 1 (2007), 33-57
- [18] Li W., Fan Y., Jiang Q., Xu Q., Velocity-Driven Particle Swarm Optimization, Proceedings of the International Conference on Computing and Pattern Recognition (ICCPR '19). Association for Computing Machinery, (2019), 9-16
- [19] Clerc M., Kennedy J., The particle swarm – explosion, stability, and convergence in a multidimensional complex space. IEEE Transaction on Evolutionary Computation, 6 (2002), No. 1, 58-73
- [20] Cleghorn C.W., Engelbrecht A.P., Fitness-distance-ratio particle swarm optimization: stability analysis, Proceedings of the Genetic and Evolutionary Computation Conference, (2017), 12-18
- [21] Fernandes C.M., Rosa A.C., Fachada N., Laredo J.L.J., Merelo J. J., Particle swarm and population structure, Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, (2018), 85-86
- [22] Peram T., Veeramachaneni K., Mohan C.K., Fitness-distanceratio based particle swarm optimization, Proceedings of the 2003 IEEE Swarm Intelligence Symposium, (2003), 174-181
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c0f08b60-92e9-4136-b37a-8b6f490fbd67