Abstract
An artificial neural network based online adaptive control of superconducting magnetic energy storage system (SMES) controller has been proposed to improve the dynamic performance of a permanent magnet synchronous generator (PMSG) wind system. The training data for the neural network has been generated through an improved particle swarm optimization (IPSO) algorithm. The weighting matrix for the radial basis function is obtained from a large input-output data set representing various operating conditions. The control parameters were updated for transient variations in the system through an adaptation procedure of the weighting functions. The proposed adaptive algorithm was tested on the PMSG system for different disturbances such as wind gust as well as low voltage condition on the grid. The adaptive radial basis function neural network (RBFNN) based SMES control exhibited excellent transient behavior following large disturbances on the wind system.
| Original language | English |
|---|---|
| Title of host publication | 2013 IEEE Power and Energy Society General Meeting, PES 2013 |
| DOIs | |
| State | Published - 2013 |
Publication series
| Name | IEEE Power and Energy Society General Meeting |
|---|---|
| ISSN (Print) | 1944-9925 |
| ISSN (Electronic) | 1944-9933 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 7 Affordable and Clean Energy
Keywords
- Adaptive RBFNN
- IPSO
- PMSG
- SMES
- Wind system
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Nuclear Energy and Engineering
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
Fingerprint
Dive into the research topics of 'An adaptive optimum SMES controller for a PMSG wind generation system'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver