NDLI: An optimal reference governor with a neural network combined model for hybrid Fuel-Cell/Gas-Turbine

Content Provider	IEEE Xplore Digital Library
Author	Wenli Yang Lee, K.Y.
Copyright Year	2010
Description	Author affiliation: Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA (Lee, K.Y.) \|\| Electrical Engineering Department, The Pennsylvania State University, University Park, 16802, USA (Wenli Yang)
Abstract	This paper introduces a concept of real-time optimization of hybrid fuel-cell power plants as an alternative distributed generation source that improves the power quality and reliability of the power grid. One of the most important issues of plant operation is the optimal control of the power plant, leading to significant economic and environmental benefits. As a commercialized fuel cell technology, Direct Fuel-Cell with Gas-Turbine (DFC/T) power plant is investigated in this paper. A framework of an optimal reference governor (ORG) is developed to generate optimal control strategies for the local controllers. For the purpose of on-line application, a neural network combined model is built as a state estimator that approximates the plant behaviors, which is compatible with population based real-time heuristic optimization algorithms. The simulation of the optimization result is presented and validated by a comparison with experimental data and simulation result of a mathematical plant model.
Starting Page	1
Ending Page	6
File Size	677849
Page Count	6
File Format	PDF
ISBN	9781424465491
ISSN	19449925
e-ISBN	9781424465514
e-ISBN	9781424483570
DOI	10.1109/PES.2010.5590197
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Optimization Mathematical model Turbines Fuels Data models heuristic optimization Fuel cells hybrid power plant fuel-cell/gas-turbine distributed generation neural networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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