| Autor: |
Jean Sawma, Eric Monmasson, Flavia Khatounian, Ragi Ghosn |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
IECON |
| DOI: |
10.1109/iecon48115.2021.9589161 |
| Popis: |
Squirrel cage induction motors are widely used in the industrial field as they present many advantages compared to other types of electric machines. They are self-starting, need little maintenance, can work in harsh environment and are relatively cheap. Many industrial applications requiring current or speed control of the machine need the knowledge of the machine parameters. Therefore, induction motor parameter identification algorithms are developed taking in consideration the accuracy of the identification process which impacts on the quality of the control and thereby the overall system. These algorithms are usually sensitive to measurement noise thus there usage in noisy industrial environment is not desirable. This paper presents three induction motor offline identification algorithms and test their accuracy and their ability to work in noisy environment. The first method subject to the study is a Least Mean Square (LMS) based identification method working at standstill, the second is a newly introduced method robust toward noises and the third is the classic no-load and blocked rotor identification method. Simulation results are presented and finally conclusions are drawn. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
