Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning

张逸佳; 任少君; 朱保宇; 翁琪航; 卫子涵; 司风琪

doi:10.19666/j.rlfd.202501029

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Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning

Thermal Power Generation Vol. 54, Issue 11, Pages: 107-116(2025)

更新时间：2026-08-11

- Modeling method for characterizing condenser system based on multi-fidelity data and transfer learning
- 作者机构：
  
  东南大学能源热转换及过程测控教育部重点实验室
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202501029
  CLC： TM621
- Published：2025
- 稿件说明：
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[1]张逸佳,任少君,朱保宇,等.基于多保真度数据和迁移学习的凝汽器系统特性建模方法[J],2025,54(11):107-116.
[1]张逸佳,任少君,朱保宇,等.基于多保真度数据和迁移学习的凝汽器系统特性建模方法[J],2025,54(11):107-116. DOI： 10.19666/j.rlfd.202501029.

DOI：10.19666/j.rlfd.202501029.

摘要

数据驱动模型有效性取决于训练样本是否完备，对于样本覆盖范围外的工况，模型泛化能力较差。为构建适应机组宽负荷变化的凝汽器系统特性模型，训练样本需包括较大变化范围的发电负荷和环境温度。然而，新建机组因投运时间短，无法提供完备数据集。因此，针对不完备数据集提出了一种基于多保真度数据和迁移学习的凝汽器系统特性建模方法。该方法首先采用同类型系统的完备数据集构建预训练模型，在预训练模型上，新增线性和非线性校准网络，通过新建机组的不完备数据对校准网络进行更新，使预训练模型迁移适配不完备数据集的特征空间。最后通过某1 000 MW超临界机组凝汽器系统验证该方法有效性，验证结果表明，在训练样本不充分的情况下，该方法能准确预测凝汽器压力和循环水出口温度等参数，决定系数R

平均值为0.95，明显优于传统单一数据集数据驱动模型的R

平均值0.81。

Abstract

The effectiveness of a data-driven model relies on the completeness of its training samples. For operating conditions beyond the scope of the training samples

the model's generalization ability is compromised. Therefore

to develop a condenser model that can adapt to the wide load variation of the unit

it is essential for the training samples to involve a diverse range of power generation loads and ambient temperatures. However

achieving this complete dataset is difficult for newly-commissioned units because of their short operation time. To address these challenges

a method for characterizing

condensing units using multi-fidelity data and transfer learning is proposed

even with incomplete data. In this method

a pre-trained model is firstly built based on the comprehensive operational dataset collected from a similar unit. On the basis of the pre-trained model

additional linear and nonlinear calibration networks are introduced. The calibration networks are updated through the incomplete data of newly constructed units

enabling the transfer of the pre-training model to the feature space that is adapted to the incomplete dataset. The effectiveness of this method is validated through the condenser of a 1 000 MW supercritical unit. The results indicate that

even with limited training samples

the method accurately predicts parameters such as condenser pressure and circulating water outlet temperature

with an average R

of 0.95

significantly outperforming the conventional data-driven model based on a single data set

of which the average R

is only 0.81.

关键词

Keywords

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