Soft measuring method and system based on kernel principal component analysis and radial basis function neural network

A technology based on neural network and kernel principal component analysis, which is applied in the field of soft measurement of operating parameters of generator sets, can solve the problems of low efficiency of extracting principal components and cannot fully reflect the characteristic information of original variables, and achieves good generalization ability, Increased safety and reliability, easy to achieve effect

Inactive Publication Date: 2015-04-22
ZHEJIANG UNIV
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Problems solved by technology

After PCA is used to extract the principal components, although the correlation between the input variables can be eliminated, the extracted principal components cannot completely reflect the characteristic information of the original variables, and the extraction efficiency of the principal components is not high.

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  • Soft measuring method and system based on kernel principal component analysis and radial basis function neural network
  • Soft measuring method and system based on kernel principal component analysis and radial basis function neural network
  • Soft measuring method and system based on kernel principal component analysis and radial basis function neural network

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Embodiment Construction

[0025] The present invention will be more fully described with reference to the accompanying drawings, in which some, but not all embodiments of the invention are shown. In fact, the present invention can be embodied in many different forms, can measure many difficult parameters, can be applied not only to generator sets, but also to many industrial processes, and it should not be seen as limited to the implementation set forth herein. example; rather, the embodiments of the present invention should be considered as provided so that the disclosure of the present invention will satisfy applicable legal requirements. The measured parameter in this embodiment is gas turbine exhaust temperature. Gas turbine exhaust temperature is an important parameter of a gas turbine. Accurate measurement of exhaust temperature is the key to accurately controlling turbine inlet temperature. If the measurement of exhaust temperature is inaccurate, set Affects the control of the turbine inlet temp...

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Abstract

The invention discloses a soft measuring system based on kernel principal component analysis and radial basis function neural network, and the system can be used to measure parameters hard to measure in a generating set or in the complex industrial process. An intelligent instrument for measuring auxiliary variables, a DCS database for storing data and the soft measuring system are included, wherein all measurable variables of the generating set are measured by the onsite intelligent instrument and stored in the DCS database, the DCS database stores all data of the set, and the soft measuring system comprises a PC used for modeling, a server for predicating a soft measurement model and a device for displaying data. The invention also discloses a soft measuring method based on kernel principal component analysis and radial basis function neural network. The system and method in the invention have high precision, generalization capability and performance, are suitable for modeling in the complex industrial process, are general and universal, can solve the problems in soft measurement of operation parameters in complex environments including high temperature, high voltage, corrosion and electromagnetic interference, and improve the system safety and reliability.

Description

technical field [0001] The invention relates to soft measurement of operating parameters of a generating set, in particular to a soft measurement method and system based on kernel principal component analysis and radial basis neural network. Background technique [0002] Many measurement sensors in modern large-scale thermal power generation units work in high-temperature, high-pressure, and corrosive complex environments, and some are also subject to strong electromagnetic interference. Data cannot be trusted. When the performance of the sensor deteriorates, fails or fails, it will have a serious impact on the subsequent monitoring, control, fault diagnosis and other systems, resulting in false alarms, misdiagnosis, and even immeasurable losses. At present, the method of physical redundancy is mainly used to ensure the accuracy of sensor measurement data in harsh environments, but in some special cases, the installation of additional sensors will be limited or the signal c...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 陈坚红李鸿坤盛德仁李蔚
Owner ZHEJIANG UNIV
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