| Full text | |
| Author(s): |
dos Santos, Tiago Henrique
[1]
;
da Silva, Ivan Nunes
[2]
;
Goedtel, Alessandro
[3]
;
Castoldi, Marcelo Favoretto
[3]
Total Authors: 4
|
| Affiliation: | [1] Fed Inst Parana, Dept Control & Ind Proc, Assis Chateaubriand, Parana - Brazil
[2] Univ Sao Paulo, Sao Carlos Sch Engn, Dept Elect Engn, Sao Carlos, SP - Brazil
[3] Fed Technol Univ Parana, Dept Elect Engn, Av Alberto Carazzai N1640, BR-86300000 Cornelio Procopio, Parana - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | ELECTRIC POWER COMPONENTS AND SYSTEMS; v. 47, n. 9-10 JULY 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
The induction motor speed is an important quantity in an industrial process and can be used indirectly in flow, pressure, and drive control. However, the direct measurement of speed compromises the driver system and control, increasing the implementation cost. Speed estimators are usually based on a mathematical model of the induction motor, but it is typically necessary to obtain the parameters of the motors. Thus, this work proposes an artificial neural network approach to estimate the mechanical speed of induction motors in a stator flux-oriented vector control by direct current control and direct torque control. In this proposed strategy, no machine parameters adaptation is needed. The neural speed estimators, without weight change, are tested in two different motors to evaluate its robustness. First, by simulation, the neural networks are trained with constant machine parameters and then the estimator performance is evaluated under stator and rotor resistance variation. After that, the same neural estimators are experimentally tested, with another machine, under the variation of motor load torque and speed reference operating point. (AU) | |
| FAPESP's process: | 11/17610-0 - Monitoring and control of dynamic systems subject to faults |
| Grantee: | Roberto Kawakami Harrop Galvão |
| Support Opportunities: | Research Projects - Thematic Grants |