A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements
- Submitting institution
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The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1386
- Type
- D - Journal article
- DOI
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10.1109/tcst.2015.2445852
- Title of journal
- IEEE Transactions on Control Systems Technology
- Article number
- -
- First page
- 703
- Volume
- 24
- Issue
- 2
- ISSN
- 1063-6536
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2015
- URL
-
-
- 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)
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D - Robotics and Embedded Systems (RES)
- Citation count
- 37
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This highly-cited (primarily by power-researchers) paper presents a hybrid filter algorithm to deal with the important state estimation problem from heterogeneous measurement data. Phase measurement units(PMU) are more precise and higher frequency than traditional sensors but systems are currently operating with a mix of PMUs and traditional sensors; causing problematic heterogeneous data. Significantly, this work developed novel methods to fuse the different data improving performance estimation. A constrained optimisation framework incorporated into a traditional extended Kalman-filtering algorithm refines state estimation using PMU measurements, providing a practical method to utilise new data without significant changes to existing power industry software tools.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -