- Title
- Advanced Model Predictive Control for AC drives with common mode voltage mitigation
- Creator
- Uddin, S. M. Muslem
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2021
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- Model Predictive Control (MPC) is a popular control strategy studied in many research publications. Moreover, its acceptance by industry is slow. This thesis identifies a class of AC application, which can significantly benefit from MPC paradigm. Thus, is high performance AC drives operating in industrial environments where common mode voltage (CMV) is a critical aspect. After critical analysis of the existing MPC-based approaches, the thesis proposes a new and advanced MPC scheme called Feedback Quantization Model Predictive Control (FBQ-MPC). The proposed scheme has a number of important improvements, including integral action, advanced disturbance rejection, improved modulation performance and control over the harmonic spectrum, as well as CMV minimization. Application of the proposed FBQ-MPC method is demonstrated with two selected power converter options found as most appropriate in CMV sensitive environment. Based on the above, full models of industrial AC drive have been developed and studied by simulation and experiment. The studies have shown that AC drives based on FBQ-MPC overcome the common MPC drawback and offer prominent advantages in CMV sensitive, as well as more general, AC drive applications.
- Subject
- Model Predictive Control; Feedback Quantization; common mode voltage; electrical machines; total harmonic distortion; electromagnetic interference; common mode current
- Identifier
- http://hdl.handle.net/1959.13/1430649
- Identifier
- uon:38866
- Rights
- Copyright 2021 S. M. Muslem Uddin
- Language
- eng
- Hits: 1424
- Visitors: 1731
- Downloads: 387
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT01 | Thesis | 36 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 2 MB | Adobe Acrobat PDF | View Details Download |