基于gcForest-AdaBoost的光伏储能并网系统暂态稳定评估

王雨辰; 李忠艳; 苏涵

doi:10.19912/j.0254-0096.tynxb.2025-0134

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (6) : 58-67. DOI: 10.19912/j.0254-0096.tynxb.2025-0134

基于gcForest-AdaBoost的光伏储能并网系统暂态稳定评估

王雨辰¹, 李忠艳¹, 苏涵²

作者信息 +

TRANSIENT STABILITY EVALUATION OF GRID-CONNECTED PHOTOVOLTAIC ENERGY STORAGE SYSTEM BASED ON gcForest-AdaBoost

Wang Yuchen¹, Li Zhongyan¹, Su Han²

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文章历史 +

摘要

针对当前光伏储能并网系统暂态稳定评估方面存在的预测精度不高问题,结合深度学习与集成学习技术,提出一种基于集成深度随机森林算法（gcForest-AdaBoost）的光伏储能并网系统暂态稳定评估方法。首先,依据光伏储能并网系统运行过程中的稳态分量构建初始输入特征;其次,为减少模型过度拟合,保证级联森林层数增加后模型仍具有较强学习能力,结合深度随机森林算法以及AdaBoost算法,提出基于gcForest-AdaBoost的暂态稳定评估方法;再次,利用输入特征集训练gcForest-AdaBoost模型,并建立光伏储能并网系统暂态稳定评估模型;最后,在IEEE 39节点系统接入光伏储能机组构建算例系统展开仿真分析和数据采集,得出评估结果。算例分析表明,所提模型能对光伏储能并网系统暂态稳定展开有效分析,且经过验证发现所提模型具有较好的鲁棒性和泛化能力。

Abstract

It is of great significance to realize transient stability evaluation accurately and quickly for stable and safe operation of power system. Aiming at the problem of low prediction accuracy in the current transient stability evaluation of grid-connected photovoltaic energy storage systems, a transient stability evaluation method based on integrated deep Random forest algorithm （gcForest-AdaBoost） is proposed by combining deep learning and ensemble learning techniques. Firstly, the initial input features are constructed according to the steady-state components of the grid-connected photovoltaic energy storage system during operation. Secondly, in order to reduce the overfit of the model and ensure that the model still has strong learning ability after the number of cascades increases, a transient stability evaluation method based on gcForest-AdaBoost is proposed by combining deep random forest algorithm and AdaBoost algorithm. Thirdly, the gcForest-AdaBoost model is trained with input feature set, and the transient stability evaluation model of grid-connected PV energy storage system is established. Finally, the IEEE 39-node system is connected to the photovoltaic energy storage unit to build a case system for simulation analysis and data acquisition, and the evaluation results are obtained. Numerical examples show that the proposed model can effectively analyze the transient stability of grid-connected photovoltaic energy storage systems, and it is found that the proposed model has good robustness and generalization ability.

导出引用

王雨辰, 李忠艳, 苏涵. 基于gcForest-AdaBoost的光伏储能并网系统暂态稳定评估[J]. 太阳能学报. 2026, 47(6): 58-67 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0134

Wang Yuchen, Li Zhongyan, Su Han. TRANSIENT STABILITY EVALUATION OF GRID-CONNECTED PHOTOVOLTAIC ENERGY STORAGE SYSTEM BASED ON gcForest-AdaBoost[J]. Acta Energiae Solaris Sinica. 2026, 47(6): 58-67 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0134

中图分类号： TM712

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