物理-数据协同驱动的规模化光伏制氢场站等值建模

摆志俊; 李瑞; 李建康; 贺成龙; 胡伟; 刘永攀; 袁铁江

doi:10.11930/j.issn.1004-9649.202507070

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物理-数据协同驱动的规模化光伏制氢场站等值建模

新能源与储能 | 更新时间：2026-07-01

- 物理-数据协同驱动的规模化光伏制氢场站等值建模
- Equivalent modeling of large-scale photovoltaic hydrogen production stations driven by physics-data collaboration
- 中国电力 2026年59卷第6期页码：154-165
- 作者机构：
  
  1. 国网新疆电力有限公司阿克苏供电公司,新疆,阿克苏,843000
  2. 大连理工大学电气工程学院,辽宁,大连,116024
- 作者简介：
- 基金信息：
  
  国网新疆电力有限公司阿克苏供电公司科技项目（规模化光伏制氢场站及送出线路继电保护的适应性研究，SGXJAK00KJJS2401009）。
- DOI：10.11930/j.issn.1004-9649.202507070
  中图分类号：
- 网络首发：2026-06-22，
  
  纸质出版：2026
- 稿件说明：
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摆志俊, 李瑞, 李建康, 贺成龙, 胡伟, 刘永攀, 袁铁江. 物理-数据协同驱动的规模化光伏制氢场站等值建模[J]. 中国电力, 2026, 59(6): 154-165.

BAI Zhijun, LI Rui, LI Jiankang, et al. Equivalent modeling of large-scale photovoltaic hydrogen production stations driven by physics-data collaboration[J]. 2026, 59(6): 154-165.
摆志俊, 李瑞, 李建康, 贺成龙, 胡伟, 刘永攀, 袁铁江. 物理-数据协同驱动的规模化光伏制氢场站等值建模[J]. 中国电力, 2026, 59(6): 154-165. DOI： 10.11930/j.issn.1004-9649.202507070.

BAI Zhijun, LI Rui, LI Jiankang, et al. Equivalent modeling of large-scale photovoltaic hydrogen production stations driven by physics-data collaboration[J]. 2026, 59(6): 154-165. DOI： 10.11930/j.issn.1004-9649.202507070.

摘要

针对传统单一驱动建模方法难以精确刻画光伏制氢场站不同运行状态下复杂动态特性的问题，提出一种物理-数据协同驱动的规模化光伏制氢场站等值建模方法。首先，基于物理机理分析提炼出能表征场站动态特性的关键因素；其次，采用稀疏自编码器对关键因素数据进行降维处理，在单相故障下单台光伏单元的数据维度可降至3维，同时信息保留率达92.2%；然后，通过谱聚类算法对降维后的数据特征进行分群，实现整个场站的等值建模；最后，在PSCAD/EMTDC平台上通过单相、三相故障仿真验证所提等值建模方法的有效性和准确性。结果表明：在单相和三相故障下，所提等值模型的有功功率响应曲线的拟合精度均优于传统物理分群法和纯数据驱动法。

Abstract

To address the problem of the traditional single-driven modeling methods being difficult to accurately characterize the complex dynamic characteristics of photovoltaic (PV) hydrogen production stations under different operating states

an equivalent modeling method for large-scale photovoltaic hydrogen production stations driven by physics-data collaboration is proposed. Firstly

key factors that can characterize the station's dynamic characteristics are extracted based on physical mechanism analysis. Secondly

a sparse autoencoder is used for dimensionality reduction of the key factor data; under single-phase fault

the data dimension of a single PV unit can be reduced to 3 dimensions with an information retention rate as high as 92.2%. Then

the spectral clustering algorithm is applied to cluster the dimensionality-reduced data features to achieve the equivalent modeling of the entire station. Finally

the effectiveness and accuracy of the proposed equivalent modeling method are verified through single-phase and three-phase fault simulations on the PSCAD/EMTDC platform. Quantitative comparison results indicate that under single and three-phase fault

the fitting accuracy of the power response curve of the proposed equivalent model is superior to the traditional physical-based grouping method and the pure data-driven method.

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Keywords

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