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Fractional-order derivatives and integrals in PID controllers of PMSM drives
Hromek, Vít ; Veselý, Libor (referee) ; Zezula, Lukáš (advisor)
This bachelor thesis focuses on the control of a permanent magnet synchronous motor (PMSM) with a fractional order PI controller (FOPI). Using vector control, the current components are decoupled ("decoupling") and controlled individually. PI controllers are then designed on these current components, which are replaced by FOPI controllers in the later part of the paper, and the quality of the control is evaluated using the quadratic integral criterion and the ITAE criterion. Similarly, a fractional order controller is also wound for speed control. The last part of the work is focused on plotting the results for different parameters. There is no improvement in the quality of current and speed control using fractional order controller over the conventional controller.
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Inter turn short-circuit detection in vector controlled PMS motor using AI
Zezula, Lukáš ; Kozovský, Matúš (referee) ; Blaha, Petr (advisor)
This thesis deals with the diagnostics of inter turn faults in a vector controlled synchronous motor with permanent magnets. Inter turn faults are detected by the pretrained convolution neural network GoogLeNet from adequately preprocessed signals of phase currents, inverter voltages and electrical angular velocity. Signal preprocesing includes, but is not limited to digital filtration, resampling and Wavelet transform. For the purpose of network training a drive system model is created, capable of simulating inter turn faults. The network is then trained on the simulated data and later validated with data measured on a real drive system, capable of emulating faults. The results of the diagnostics, together with the main problems are presented in the conclusion.
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Inter turn short-circuit detection in vector controlled PMS motor using AI
Zezula, Lukáš ; Kozovský, Matúš (referee) ; Blaha, Petr (advisor)
This thesis deals with the diagnostics of inter turn faults in a vector controlled synchronous motor with permanent magnets. Inter turn faults are detected by the pretrained convolution neural network GoogLeNet from adequately preprocessed signals of phase currents, inverter voltages and electrical angular velocity. Signal preprocesing includes, but is not limited to digital filtration, resampling and Wavelet transform. For the purpose of network training a drive system model is created, capable of simulating inter turn faults. The network is then trained on the simulated data and later validated with data measured on a real drive system, capable of emulating faults. The results of the diagnostics, together with the main problems are presented in the conclusion.
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