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contributor authorXiao-Juan Wu
contributor authorQi Huang
contributor authorXin-Jian Zhu
contributor authorChang-Hua Zhang
date accessioned2017-05-09T00:44:35Z
date available2017-05-09T00:44:35Z
date copyrightOctober, 2011
date issued2011
identifier issn2381-6872
identifier otherJFCSAU-28950#051009_1.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/146438
description abstractTransients in a load have a significant impact on the performance and durability of a solid oxide fuel cell (SOFC) integrated into a micro gas turbine (MGT) hybrid power system. One of the main reasons is that the SOFC operating temperature and turbine inlet temperature change drastically due to the load change. Therefore, in order to guarantee the temperature to operate within a specified range, an adaptive proportional-integral-derivative (PID) decoupling control strategy based on a dynamic radial basis function (RBF) neural network is presented to control the temperature of a natural gas fueled, tubular SOFC/MGT hybrid with internal reforming in this paper. Using the self-learning ability of the dynamic RBF neural network, the proportional, integral, and differential factor of the PID controller are tuned on-line. The simulation results show that it is feasible to build the adaptive PID decoupling controller for temperature control of the SOFC/MGT hybrid system.
publisherThe American Society of Mechanical Engineers (ASME)
titleTemperature Control of a SOFC and MGT Hybrid System
typeJournal Paper
journal volume8
journal issue5
journal titleJournal of Fuel Cell Science and Technology
identifier doi10.1115/1.4004174
journal fristpage51009
identifier eissn2381-6910
keywordsSolid oxide fuel cells
keywordsTemperature control
keywordsTemperature
keywordsTurbines
keywordsArtificial neural networks
keywordsControl equipment AND Operating temperature
treeJournal of Fuel Cell Science and Technology:;2011:;volume( 008 ):;issue: 005
contenttypeFulltext


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