Induction Motor Parameter Estimation Using Sparse Grid Optimization Algorithm
- Submitting institution
-
London South Bank University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 164991
- Type
- D - Journal article
- DOI
-
10.1109/TII.2016.2573743
- Title of journal
- IEEE Transactions on Industrial Informatics
- Article number
- -
- First page
- 1453
- Volume
- 12
- Issue
- 4
- ISSN
- 1941-0050
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2016
- URL
-
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7479570
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
B - Cognitive Systems Research Centre
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper extends the hyperbolic cross point (HCP) based global optimization to parameters estimation. This work has been widely recognized as a low-cost and non-invasive method for parameter estimation as it uses only external measurements, resulting in reduced system complexity and cost. Although this paper focussed on induction motor parameter estimation for efficient motor control, we are using the method for wind turbine and electrical vehicle parameter estimation and condition monitoring. Further applications are under investigation with industrial patterners.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -