A modeling methodology for robust stability analysis of nonlinear electrical power systems under parameter uncertainties
This paper develops a modelling method for robust stability analysis of non-linear electrical power systems over a range of operating points and under parameter uncertainties. Standard methods can guarantee stability under nominal condi¬tions but do not take into account any uncertainties of the mod...
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IEEE
2016
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| Online Access: | https://eprints.nottingham.ac.uk/37789/ |
| _version_ | 1848795534946467840 |
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| author | Sumsurooah, Sharmila Odavic, Milijana Bozhko, Serhiy |
| author_facet | Sumsurooah, Sharmila Odavic, Milijana Bozhko, Serhiy |
| author_sort | Sumsurooah, Sharmila |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This paper develops a modelling method for robust stability analysis of non-linear electrical power systems over a range of operating points and under parameter uncertainties. Standard methods can guarantee stability under nominal condi¬tions but do not take into account any uncertainties of the model. In this work, stability is assessed by using structured singular value (SSV) analysis also known as analysis. This method provides a measure of stability robustness of linear systems against all considered sources of structured uncertainties. The aim of this work is to apply the SSV method for robust small-signal analysis of non-linear systems over a range of operating points and parameter variations. To that end, a modelling methodology is developed to represent any such system with an equivalent linear model that contains all system variabil¬ity, in addition to being suitable for analysis. The method employs symbolic linearisation around an arbitrary operating point. Furthermore, in order to reduce conservativeness in the stability assessment of the non-linear system, the approach takes into account dependencies of operating points on parameter variations. The methodology is verified through analysis of the equivalent linear model of a 4 kW permanent magnet machine drive, which successfully predicts the destabilising torque over a range of different operating points and under parameter variations. Further, the predictions from analysis are validated against experimental results. |
| first_indexed | 2025-11-14T19:33:37Z |
| format | Article |
| id | nottingham-37789 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:33:37Z |
| publishDate | 2016 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-377892020-05-04T20:01:23Z https://eprints.nottingham.ac.uk/37789/ A modeling methodology for robust stability analysis of nonlinear electrical power systems under parameter uncertainties Sumsurooah, Sharmila Odavic, Milijana Bozhko, Serhiy This paper develops a modelling method for robust stability analysis of non-linear electrical power systems over a range of operating points and under parameter uncertainties. Standard methods can guarantee stability under nominal condi¬tions but do not take into account any uncertainties of the model. In this work, stability is assessed by using structured singular value (SSV) analysis also known as analysis. This method provides a measure of stability robustness of linear systems against all considered sources of structured uncertainties. The aim of this work is to apply the SSV method for robust small-signal analysis of non-linear systems over a range of operating points and parameter variations. To that end, a modelling methodology is developed to represent any such system with an equivalent linear model that contains all system variabil¬ity, in addition to being suitable for analysis. The method employs symbolic linearisation around an arbitrary operating point. Furthermore, in order to reduce conservativeness in the stability assessment of the non-linear system, the approach takes into account dependencies of operating points on parameter variations. The methodology is verified through analysis of the equivalent linear model of a 4 kW permanent magnet machine drive, which successfully predicts the destabilising torque over a range of different operating points and under parameter variations. Further, the predictions from analysis are validated against experimental results. IEEE 2016-09 Article PeerReviewed Sumsurooah, Sharmila, Odavic, Milijana and Bozhko, Serhiy (2016) A modeling methodology for robust stability analysis of nonlinear electrical power systems under parameter uncertainties. IEEE Transactions on Industry Applications, 52 (5). pp. 4416-4425. ISSN 1939-9367 Robust stability analysis Linear fractional transformation Structured singular value analysis http://ieeexplore.ieee.org/document/7492245/ doi:10.1109/TIA.2016.2581151 doi:10.1109/TIA.2016.2581151 |
| spellingShingle | Robust stability analysis Linear fractional transformation Structured singular value analysis Sumsurooah, Sharmila Odavic, Milijana Bozhko, Serhiy A modeling methodology for robust stability analysis of nonlinear electrical power systems under parameter uncertainties |
| title | A modeling methodology for robust stability analysis of nonlinear electrical power systems under parameter uncertainties |
| title_full | A modeling methodology for robust stability analysis of nonlinear electrical power systems under parameter uncertainties |
| title_fullStr | A modeling methodology for robust stability analysis of nonlinear electrical power systems under parameter uncertainties |
| title_full_unstemmed | A modeling methodology for robust stability analysis of nonlinear electrical power systems under parameter uncertainties |
| title_short | A modeling methodology for robust stability analysis of nonlinear electrical power systems under parameter uncertainties |
| title_sort | modeling methodology for robust stability analysis of nonlinear electrical power systems under parameter uncertainties |
| topic | Robust stability analysis Linear fractional transformation Structured singular value analysis |
| url | https://eprints.nottingham.ac.uk/37789/ https://eprints.nottingham.ac.uk/37789/ https://eprints.nottingham.ac.uk/37789/ |