Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS
This paper presents a supervisory control unit (SCU) combined with short-term ahead wind speed prediction for proper and effective management of the stored energy in a small capacity flywheel energy storage system (FESS) which is used to mitigate the output power fluctuations of an aggregated wind f...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
The Institute of Electrical and Electronic Engineers (IEEE)
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/18118 |
| _version_ | 1848749652938063872 |
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| author | Islam, F. Al-Durra, A. Muyeen, S.M. |
| author_facet | Islam, F. Al-Durra, A. Muyeen, S.M. |
| author_sort | Islam, F. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents a supervisory control unit (SCU) combined with short-term ahead wind speed prediction for proper and effective management of the stored energy in a small capacity flywheel energy storage system (FESS) which is used to mitigate the output power fluctuations of an aggregated wind farm. Wind speed prediction is critical for a wind energy conversion system since it may greatly influence the issues related to effective energy management, dynamic control of wind turbine, and improvement of the overall efficiency of the power generation system. In this study, a wind speed prediction model is developed by artificial neural network (ANN) which has advantages over the conventional prediction schemes including data error tolerance and ease in adaptability. The proposed SCU-based control would help to reduce the size of the energy storage system for minimizing wind power fluctuation taking the advantage of prediction scheme. The model for prediction using ANN is developed in MATLAB/Simulink and interfaced with PSCAD/EMTDC. Effectiveness of the proposed control system is illustrated using real wind speed data in various operating conditions. |
| first_indexed | 2025-11-14T07:24:21Z |
| format | Journal Article |
| id | curtin-20.500.11937-18118 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:24:21Z |
| publishDate | 2013 |
| publisher | The Institute of Electrical and Electronic Engineers (IEEE) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-181182017-09-13T16:10:40Z Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS Islam, F. Al-Durra, A. Muyeen, S.M. This paper presents a supervisory control unit (SCU) combined with short-term ahead wind speed prediction for proper and effective management of the stored energy in a small capacity flywheel energy storage system (FESS) which is used to mitigate the output power fluctuations of an aggregated wind farm. Wind speed prediction is critical for a wind energy conversion system since it may greatly influence the issues related to effective energy management, dynamic control of wind turbine, and improvement of the overall efficiency of the power generation system. In this study, a wind speed prediction model is developed by artificial neural network (ANN) which has advantages over the conventional prediction schemes including data error tolerance and ease in adaptability. The proposed SCU-based control would help to reduce the size of the energy storage system for minimizing wind power fluctuation taking the advantage of prediction scheme. The model for prediction using ANN is developed in MATLAB/Simulink and interfaced with PSCAD/EMTDC. Effectiveness of the proposed control system is illustrated using real wind speed data in various operating conditions. 2013 Journal Article http://hdl.handle.net/20.500.11937/18118 10.1109/TSTE.2013.2256944 The Institute of Electrical and Electronic Engineers (IEEE) fulltext |
| spellingShingle | Islam, F. Al-Durra, A. Muyeen, S.M. Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS |
| title | Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS |
| title_full | Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS |
| title_fullStr | Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS |
| title_full_unstemmed | Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS |
| title_short | Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS |
| title_sort | smoothing of wind farm output by prediction and supervisory-control-unit- based fess |
| url | http://hdl.handle.net/20.500.11937/18118 |