Multimodel Parameters Identification for Main Steam Temperature of Ultra-Supercritical Units Using an Improved Genetic Algorithm

Hu, Wenkai; Fang, Yanjun

doi:10.1061/(asce)ey.1943-7897.0000105

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…In fact, the search effect of evolutionary algorithm is affected by the number of search targets. For example, in literature, 24 if the number of targets is small, good effect can be obtained. In USC units, multiple influence factors from feed-water, feed-fuel to generator determine it as a multi-variable system where the total number of A, B, C up to 27, resulting in the rise of “curse of dimensionality.” In this case, the idea of learning in batches is introduced in the study, where the purpose of global identification is achieved through batch identification, and therefore, the difficulty that evolutionary algorithm can not directly apply to multi-variable system identification is flexibly solved.…”

Section: Modeling Processmentioning

confidence: 99%

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Generalized predictive control of ultra-supercritical power plant by using a composite weighted human learning optimization network

Cheng

Peng

Zeng

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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The ultra-supercritical power plant is an advanced energy conversion system with high coal utilization and low emission. It is a complex system associated with high nonlinearity, strong coupling, and subjected to various uncertainties and interferences. This article introduces a nonlinear generalized predictive control based on a composite weighted human learning optimization network to incorporate both local linear modeling and local control into a multi-layer dynamic structure. Since composite weighted human learning optimization network can be considered as a linear-in-weight network, the limitation of the non-convex is elaborately broken through incorporating composite weighted human learning optimization models into the convex quadratic programming routine of local linear generalized predictive control. Detailed analysis is given to show the validity and advantage of the proposed method in controlling the ultra-supercritical power plant.

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Section: Modeling Processmentioning

confidence: 99%

“…For example, in literature, 24 if the number of targets is small, good effect can be obtained. In USC units, multiple influence factors from feed-water, feed-fuel to generator determine it as a multi-variable system where the total number of A, B, C up to 27, resulting in the rise of ''curse of dimensionality.''…”

Section: Learning Of Cwhlomentioning

confidence: 99%