Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm

The flexible operation capability of combined heat and power (CHP) unit under full operating conditions must be promoted to suppress the power grid fluctuation in large-scale integration of renewable energy. However, obtaining CHP unit model under large-scale variable loads and deep peak shaving is extremely challenging. To this end, an intelligent modeling method incorporating improved crossformer and precise sparrow search algorithm is designed to provide reference for flexible controller design. Firstly, to address the strong time dependence of CHP unit boiler-turbine coupling process, a bidirectional gated recurrent unit is employed as the position encoding to extract the hidden temporal feature. Secondly, a novel encoder structure with multi-receptive field convolution module is designed to enhance the local feature extraction capability of the network, which is conducive to the analysis of multivariable coupling characteristics of CHP unit. On this basis, a precise sparrow search algorithm with stronger search ability is formed by combining Tent chaotic mapping, osprey optimization algorithm, Cauchy mutation and quantum behavior, which provides support for the dynamic characteristics analysis of CHP unit. The time dependence, local feature information and optimal parameter settings are fully considered in the comprehensive modeling scheme, which results in an accurate identification model for CHP unit under full operating conditions. Finally, the effectiveness of the proposed method is verified based on the actual operating data of a 350 MW CHP unit. The accuracy of established model is greater than 99.4 %, which can well reflect the nonlinear characteristics of the unit. Therefore, the built model can be used for controller design and simulation analysis. • An improved crossformer model combined BiGRU is proposed. • A MFC module is developed to capture multivariable coupling feature of CHP unit. • The PSSA is established to obtain the optimal hyperparameters of crossformer. • The CHP unit model under full operating conditions is successfully gained.

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Publication Details

Journal
Energy
Published
2024-08-23
DOI
https://doi.org/10.1016/j.energy.2024.132879
Citations
13
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
2.71

Funders

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article

Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm

Ting Huang, Congzhi Huang, Guolian Hou, Lingling Ye
13 citations
Energy
Energy Load and Power Forecasting
2.71
article

Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm

Ting Huang, Congzhi Huang, Guolian Hou, Lingling Ye
article en
13 citations

Abstract

The flexible operation capability of combined heat and power (CHP) unit under full operating conditions must be promoted to suppress the power grid fluctuation in large-scale integration of renewable energy. However, obtaining CHP unit model under large-scale variable loads and deep peak shaving is extremely challenging. To this end, an intelligent modeling method incorporating improved crossformer and precise sparrow search algorithm is designed to provide reference for flexible controller design. Firstly, to address the strong time dependence of CHP unit boiler-turbine coupling process, a bidirectional gated recurrent unit is employed as the position encoding to extract the hidden temporal feature. Secondly, a novel encoder structure with multi-receptive field convolution module is designed to enhance the local feature extraction capability of the network, which is conducive to the analysis of multivariable coupling characteristics of CHP unit. On this basis, a precise sparrow search algorithm with stronger search ability is formed by combining Tent chaotic mapping, osprey optimization algorithm, Cauchy mutation and quantum behavior, which provides support for the dynamic characteristics analysis of CHP unit. The time dependence, local feature information and optimal parameter settings are fully considered in the comprehensive modeling scheme, which results in an accurate identification model for CHP unit under full operating conditions. Finally, the effectiveness of the proposed method is verified based on the actual operating data of a 350 MW CHP unit. The accuracy of established model is greater than 99.4 %, which can well reflect the nonlinear characteristics of the unit. Therefore, the built model can be used for controller design and simulation analysis. • An improved crossformer model combined BiGRU is proposed. • A MFC module is developed to capture multivariable coupling feature of CHP unit. • The PSSA is established to obtain the optimal hyperparameters of crossformer. • The CHP unit model under full operating conditions is successfully gained.

EnergyVol. 308
North China Electric Power University (CN)
National Natural Science Foundation of China, National University's Basic Research Foundation of China, Fundamental Research Funds for the Central Universities
Affordable and clean energy
Openalex Percentile: Top 9%
Energy Load and Power Forecasting
2.71
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