permanent magnet motors
Optimal current tracking performance is crucial for the control of high-speed permanent magnet synchronous motors (PMSMs). In this study, an innovative discrete model-free predictive current control (MFPCC) method is proposed to address the challenges presented by flux-weakening (FW) and low-frequency ratio (LFR) conditions, which limit the conventional MFPCC's applicability in the high-speed range.
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This dataset contains two subjects. The first one is a Matlab App created for PMSMs (Permanent Magnet Synchronous Motors) parameters estimation, both in the electrical and in the mechanical energetic domains. The second one is a generalized PMSM Simulink superblock equipped with a user-friendly interface allowing to select options and to input the model parameters. The Matlab App and the PMSM superblock can be easily interfaced with each other.
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This dataset was prepared to estimate the winding temperature of a BLDC motor for a variable load and speed profile. It contains two files. The first one is the measurement results for the motor without cooling, while the second one is the measurement results after installing an additional cooling fan on the shaft. The data included in the files are time stamp, winding temperature, casing temperature, speed, current, power loss, mean and standard deviation of the measured quantities for 14400 data records.
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Supplementary Material for IEEE-TII Transaction Article "Controller Design for Electrical Drives by Deep Reinforcement Learning - a Proof of Concept"
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