数据驱动的电力系统动态安全评估研究综述

Review on Data-driven Dynamic Security Assessment of Power Systems

大规模新能源和柔性负荷的接入使得电网运行方式不确定性增加,电网运行状态频繁接近稳定极限,各类动态安全风险突出。作为一种高效评估方法,数据驱动的动态安全评估(dynamic security assessment,DSA)能够直接获取输入特征与动态安全输出间的映射关系,快速发现各类动态安全风险,是提高电网安全防御水平的重要手段。随着电力大数据和人工智能技术的发展,各类先进的机器学习方法被广泛探索并应用于电力系统DSA。按照数据驱动的DSA流程,从特征选择、模型训练、模型更新、在线评估等四方面综述了现有研究进展,并总结了数据驱动的DSA面临的五大挑战。

The integration of large-scale new energy and flexible loads increases the uncertainty of power grid operation,and the operating status of the power grid frequently approaches the stability limit,highlighting various dynamic security risks.As an efficient assessment method,data-driven dynamic security assessment(DSA)can directly obtain the mapping relationship between input features and dynamic security output,which can timely discover various dynamic security risks.It is an important means to improve the intelligent defense level of the power system.With the development of power big data and artificial intelligence techniques,various cutting-edge machine learning methods are explored and applied to the DSA.According to the procedure of data-driven DSA,the current research status was reviewed in terms of four perspectives:feature selection,model training,model updating,and online assessment.Moreover,five major challenges were summarized.