| 林跻云,陶鹏,吕云彤,冀明,霍伟,方德康,明昊.基于物理信息正则化的风电功率混合计算建模方法[J].电力需求侧管理,2026,28(3):79-88 |
| 基于物理信息正则化的风电功率混合计算建模方法 |
| A hybrid wind power estimation modeling method based on physics-informed regularization |
| 投稿时间：2026-01-13 修订日期：2026-03-12 |
| DOI：10.3969/j.issn.1009-1831.2026.03.012 |
| 中文关键词: 风电功率实时计算 物理信息正则化 样本权重优化 混合建模 残差学习 物理约束机器学习 |
| 英文关键词: wind power estimation physics-informed regularization sample weight optimization hybrid modeling residual learning physics-constrained machine learning |
| 基金项目:国网河北省电力有限公司科技项目(5204YF24000M) |
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| 中文摘要: |
| 针对风电功率实时计算中物理模型精度不足、数据驱动模型缺乏物理约束的问题,提出一种结合物理建模与机器学习的混合计算方法。通过构建物理模型-机器学习残差校正-物理信息正则化3层架构,实现计算精度与物理可信度的统一。设计了基于样本权重调整的物理信息正则化策略,定义单调性、密度一致性、功率系数3个物理约束,通过迭代优化在不修改集成学习算法内核的前提下实现物理约束嵌入。采用蒙特卡洛模拟处理湍流影响下的功率期望值,结合空气密度修正、偏航效率、尾流效应等物理因子建立基准物理模型;构建多层次特征体系,采用随机森林与梯度提升树集成学习框架捕捉系统性偏差。实验结果表明,该方法相比纯物理模型和纯集成学习模型在计算精度上均有显著提升,同时有效抑制了违背物理规律的计算结果,为风电功率实时计算提供了新的技术路径。 |
| 英文摘要: |
| Addressing the issues of insufficient accuracy in physical models and lack of physical constraints in data-driven models for real-time wind power estimation, a hybrid estimation method that integrates physical modeling with machine learning is proposed. A three-layer architecture comprising physical modeling, machine learning residual correction, and physics-informed regularization is constructed to unify estimation accuracy and physical credibility. A physics-informed regularization strategy based on sample weight adjustment is designed, which defines three physical constraints including monotonicity, density consistency, and power coefficient. Through iterative optimization, physical constraint embedding is achieved without modifying the kernel of ensemble learning algorithms. Monte Carlo simulation is employed to process power expectation values under turbulence effects, and combined with physical factors such as air density correction, yaw efficiency, and wake effects, a baseline physical model is established. A multi-level feature system is constructed, utilizing random forest and gradient boosting trees ensemble learning framework to capture systematic biases. Experimental results demonstrate that the proposed method achieves significant improvements in estimation accuracy compared with both pure physical models and pure ensemble learning model. Meanwhile, estimation results that violate physical laws are effectively suppressed, providing a new technical approach for real-time wind power estimation. |
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