Load frequency controller design based on extreme learning machine
The loading of a power system is never constant. The actual load change of the power system cannot be predicted at any point in time. A load change in any are of the power system will result in a change in frequency of the power system. Frequency is a major stability criterion for large-scale multi...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2009
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| Online Access: | http://hdl.handle.net/10356/18882 |
| _version_ | 1811696384405929984 |
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| author | Wu, Si. |
| author2 | Wang Youyi |
| author_facet | Wang Youyi Wu, Si. |
| author_sort | Wu, Si. |
| collection | NTU |
| description | The loading of a power system is never constant. The actual load change of the power system cannot be predicted at any point in time. A load change in any are of the power system will result in a change in frequency of the power system. Frequency is a major stability criterion for large-scale multi area systems. To improve the stability of the power networks, it is necessary to design a load frequency control system. The designed controller must be able to cope with parametric uncertainties and nonlinearity of a real power system.
In this dissertation, a load frequency controller based on the Riccati-equation approach designed by the author’s supervisor Dr. Wang Youyi will be introduced. Only the bounds of the system parameters are required to design this controller. Simulation results show that the robust load frequency controller can ensure that the system is stable for all admissible uncertainties, even in the presence of generation rate constraint.
In the following part of the dissertation, it is proposed to use a neural network controller based on ELM (Extreme Learning Machine) algorithm instead of the robust load frequency controller. The performance data of the robust controller is set as the training pairs for the ELM neural-net load frequency controller. It aims to get a more adaptive control system for a larger parameters range. The performance of the ELM neural network load frequency controller is then compare with the original robust load frequency controller in Chapter 5 of this dissertation. |
| first_indexed | 2024-10-01T07:38:30Z |
| format | Thesis |
| id | ntu-10356/18882 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:38:30Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | ntu-10356/188822023-07-04T16:33:12Z Load frequency controller design based on extreme learning machine Wu, Si. Wang Youyi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering The loading of a power system is never constant. The actual load change of the power system cannot be predicted at any point in time. A load change in any are of the power system will result in a change in frequency of the power system. Frequency is a major stability criterion for large-scale multi area systems. To improve the stability of the power networks, it is necessary to design a load frequency control system. The designed controller must be able to cope with parametric uncertainties and nonlinearity of a real power system. In this dissertation, a load frequency controller based on the Riccati-equation approach designed by the author’s supervisor Dr. Wang Youyi will be introduced. Only the bounds of the system parameters are required to design this controller. Simulation results show that the robust load frequency controller can ensure that the system is stable for all admissible uncertainties, even in the presence of generation rate constraint. In the following part of the dissertation, it is proposed to use a neural network controller based on ELM (Extreme Learning Machine) algorithm instead of the robust load frequency controller. The performance data of the robust controller is set as the training pairs for the ELM neural-net load frequency controller. It aims to get a more adaptive control system for a larger parameters range. The performance of the ELM neural network load frequency controller is then compare with the original robust load frequency controller in Chapter 5 of this dissertation. Master of Science (Power Engineering) 2009-07-22T08:00:53Z 2009-07-22T08:00:53Z 2008 2008 Thesis http://hdl.handle.net/10356/18882 en 76 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering Wu, Si. Load frequency controller design based on extreme learning machine |
| title | Load frequency controller design based on extreme learning machine |
| title_full | Load frequency controller design based on extreme learning machine |
| title_fullStr | Load frequency controller design based on extreme learning machine |
| title_full_unstemmed | Load frequency controller design based on extreme learning machine |
| title_short | Load frequency controller design based on extreme learning machine |
| title_sort | load frequency controller design based on extreme learning machine |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering |
| url | http://hdl.handle.net/10356/18882 |
| work_keys_str_mv | AT wusi loadfrequencycontrollerdesignbasedonextremelearningmachine |