High-frequency modelling of variable-frequency drive systems based on physics-informed neural network
Variable-frequency drive (VFD) systems are found in many operations around the world because they offer an outstanding efficiency when it comes to power conversion. Despite that, these systems may cause high-frequency phenomena such as overvoltage ringing within the motor which may lead to acc...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176429 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Variable-frequency drive (VFD) systems are found in many operations around
the world because they offer an outstanding efficiency when it comes to power
conversion. Despite that, these systems may cause high-frequency phenomena
such as overvoltage ringing within the motor which may lead to accelerated
aging and even breakdowns. In order to mitigate or eradicate the effects, high frequency modeling of VFD systems have been used to alleviate the design
process. This project aims to develop a physics-informed neural network
(PINN) model that can precisely gain equivalent circuit parameters of VFD
systems to accurately predict high-frequency phenomena in VFD systems by
training the prediction model using the measured impedances of the system. |
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