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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yousefian, R. Kamalasadan, S. |
| Copyright Year | 2013 |
| Description | Author affiliation: Electr. & Comput. Eng. Dept., Univ. of North Carolina at Charlotte, Charlotte, NC, USA (Yousefian, R.; Kamalasadan, S.) |
| Abstract | The main objective of this paper is to design and implement secured coordinated control and optimization algorithms and test it for improved power system dynamic stability. The method is to design a brain like control and coordination architecture using optimal control that are modular. Then an intelligent agent knowledge base is developed that can interact with each of the intelligent controllers and optimization algorithms on each bus in the power system providing multi-level and multi-objective capabilities. Such a robust intelligent coordinated controllers with fully functional and fault tolerant Multi-Agent Methodology (MAM) architecture are the important back-bone of next generation smart grid. The main advantage of such a design is its ability to make smart and intelligent control decisions in the presence of drastic system nonlinear movement. |
| Sponsorship | IEEE Signal Process. Soc. |
| Starting Page | 1 |
| Ending Page | 7 |
| File Size | 1287182 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467348942 |
| e-ISBN | 9781467348966 |
| e-ISBN | 9781467348959 |
| DOI | 10.1109/ISGT.2013.6497921 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-02-24 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Intelligent Adaptive Control Power system stabilizer Artificial neural networks Computer architecture Intelligent supervisory loop Power system stability Generators Feed Forward Neural Network Mathematical model System-centric controller |
| Content Type | Text |
| Resource Type | Article |
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