XVII International Conference on Systems, Automatic Control and Measurements, SAUM 2024 (pp. 185-188)
АУТОР(И) / AUTHOR(S): Jovan Vukašinović , Nebojša Mitrović , Saša Štatkić , Bojan Banković , Filip Filipović
Download Full Pdf
DOI: 10.46793/SAUM24.185V
САЖЕТАК / ABSTRACT:
In this paper, experimental verification of the numerical optimization method two-stage PSO algorithm (TSPSO) for parameter identification of a cage induction motor using nominal motor data related to direct grid supply was performed. The TSPSO algorithm consists of two stages: the first stage estimates parameters for the rated operating mode, while the second stage estimates rotor parameters during motor startup. The TSPSO algorithm indirectly applies an approximation of the rotor parameter change as a function of speed to consider the influence of the skin effect at motor start-up and achieves the connection between the two stages. For the proper operation of control algorithms in frequency converters, knowledge of the parameters of the induction motor is necessary. Regulated electric drives use induction motors and frequency converters from various manufacturers. For this reason, different procedures for motor parameter identification are applied, which are integrated into the frequency converters. In this study, an experimental method was used for parameter identification of the cage induction motor based on the application of an offline procedure in a frequency converter for the identification of parameters of the cage induction motor in a standstill state. In this paper, independent parameter identification of the cage induction motor with the same nominal data was performed using two different methods. The results obtained show a certain level of agreement in the parameter values of the equivalent circuit, thereby experimentally verifying the numerical TSPSO algorithm.
КЉУЧНЕ РЕЧИ / KEYWORDS:
parameter identification, induction motor, particle swarm optimization, two-stage optimization, offline parameter identification
ПРОЈЕКАТ/ ACKNOWLEDGEMENT:
This work was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia [grant number 451-03-65/2024-03/200155].
ЛИТЕРАТУРА / REFERENCES:
- Vukašinović, S. Štatkić, M. Milovanović, N. Arsić, B. Perović, Combined method for the cage induction motor parameters estimation using two-stage PSO algorithm. Electrical Engineering. 2023. 105, pp.2703–2714.
- A. Jirdehi, A. Rezaei, Parameters estimation of squirrel-cage induction motors using ann and anfis. Alexandria Engineering Journal. 2016. 55, pp.357-368.
- Mahmoud M. Elkholy, Enas A. El-Hay, Attia A.El-Fergany, Synergy of electrostatic discharge optimizer and experimental verification for parameters estimation of three phase induction motors. Engineering Science and Technology, an International Journal. 2022. 31, pp.101067.
- F. V. Amaral, J. M. R. Baccarini, F. C. R. Coelho, L. M. Rabelo, A high precision method for induction machine parameters estimation from manufacturer data. IEEE Transactions on Energy Conversion. 2020. 36, pp.1226-1233.
- Operating Guide VLT Automation Drive FC 301/302. https://files.danfoss.com/download/Drives/MG33AT02.pdf
- Facts Worth Knowing about AC Drives. https://assets.danfoss.com/documents/latest/242341/AV446558536912en-000101.pdf
- P. Reddy and U. Loganathan, Offline Recursive Identification of Electrical Parameters of VSI-Fed Induction Motor Drives. IEEE Transactions on Power Electronics. 2020. 35, pp. 10711-10719.
- Shen, K. Wang, W. Yao, K. Lee and Z. Lu, DC biased stimulation method for induction motor parameters identification at standstill without inverter nonlinearity compensation. 2013 IEEE Energy Conversion Congress and Exposition, Denver, CO, USA, 2013, pp. 5123-5130.
- Pellegrino, P. Guglielmi, E. Armando and R. I. Bojoi, Self-Commissioning Algorithm for Inverter Nonlinearity Compensation in Sensorless Induction Motor Drives, IEEE Transactions on Industry Applications. 2010. 46, pp. 1416-1424.
- Peretti, M. Zigliotto, Automatic procedure for induction motor parameter estimation at standstill. IET Electric Power Applications. 2012. 6, pp.214-224.
- Tang, Y. Yang, F. Blaabjerg, J. Chen, L. Diao, Z. Liu, Parameter Identification of Inverter-Fed Induction Motors: A Review. Energies 2018. 11, 2194.