Identification and application of data-driven superheated steam temperature system

Superheated steam temperature is one of the important parameters of thermal power plants, and its non-linearity and large inertia pose major challenges for modeling. This article is based on data-driven concepts and focuses on field data. Fading memory recursive least square algorithm was used to es...

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Veröffentlicht in:Chinese Control and Decision Conference S. 1696 - 1701
Hauptverfasser: Weng, Jiang, Wang, Yinsong, Sun, Tianshu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2020
Schlagworte:
Analytical models
Data models
Data-driven
Fading channels
Fading memory recursive least square
Indexes
Mathematical model
Model identification
Power generation
Superheated steam temperature system
Temperature control
ISSN:1948-9447
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Zusammenfassung:Superheated steam temperature is one of the important parameters of thermal power plants, and its non-linearity and large inertia pose major challenges for modeling. This article is based on data-driven concepts and focuses on field data. Fading memory recursive least square algorithm was used to establish a desuperheater model and a superheater model based on the input and output data of the superheated steam temperature control system. In addition, three evaluation indexes are introduced to compare performance with traditional identification methods. This paper uses a multi-stage superheater simulation platform and based on the actual operating data of a power plant for verification and investigation. Practice has shown that the model identified in this paper has better accuracy.
ISSN:1948-9447
DOI:10.1109/CCDC49329.2020.9164484