Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage
This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) to enhance the transient stability of a grid-connected wind generator system. The control strategy of the SMES unit is developed based on cascaded control scheme of a voltag...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/7614 |
| _version_ | 1848745420535103488 |
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| author | Hasanien, H. Ali, S. Muyeen, S.M. |
| author_facet | Hasanien, H. Ali, S. Muyeen, S.M. |
| author_sort | Hasanien, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) to enhance the transient stability of a grid-connected wind generator system. The control strategy of the SMES unit is developed based on cascaded control scheme of a voltage source converter and a two-quadrant DC-DC chopper using insulated gate bipolar transistors (IGBTs). The proposed controller is used to control the duty cycle of the DC-DC chopper. Detailed modeling and control strategies of the system are presented. The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of a conventional proportional-integral (PI)-controlled SMES. The validity of the proposed system is verified with the simulation results which are performed using the standard dynamic power system simulator PSCAD/EMTDC. |
| first_indexed | 2025-11-14T06:17:05Z |
| format | Conference Paper |
| id | curtin-20.500.11937-7614 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:17:05Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-76142017-08-31T04:41:33Z Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage Hasanien, H. Ali, S. Muyeen, S.M. This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) to enhance the transient stability of a grid-connected wind generator system. The control strategy of the SMES unit is developed based on cascaded control scheme of a voltage source converter and a two-quadrant DC-DC chopper using insulated gate bipolar transistors (IGBTs). The proposed controller is used to control the duty cycle of the DC-DC chopper. Detailed modeling and control strategies of the system are presented. The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of a conventional proportional-integral (PI)-controlled SMES. The validity of the proposed system is verified with the simulation results which are performed using the standard dynamic power system simulator PSCAD/EMTDC. 2012 Conference Paper http://hdl.handle.net/20.500.11937/7614 fulltext |
| spellingShingle | Hasanien, H. Ali, S. Muyeen, S.M. Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage |
| title | Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage |
| title_full | Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage |
| title_fullStr | Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage |
| title_full_unstemmed | Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage |
| title_short | Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage |
| title_sort | wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage |
| url | http://hdl.handle.net/20.500.11937/7614 |