摘要
快速、准确地识别电力系统暂态稳定态势,是保证大电网安全稳定运行的重要前提。相较于传统物理解析方法,基于数据驱动的电力系统暂态稳定评估技术在解决复杂非线性映射和快速评估方面具有较大的优势,已成为目前电力系统暂态稳定评估技术研究的重要方向。该文首先结合电力系统暂态稳定评估场景需求和通用智能应用框架建立基于数据驱动的暂态稳定评估技术的基本架构,从离线训练、在线应用和反馈更新的维度分析数据驱动下各个流程环节的功能;其次,围绕着数据增强、机器学习算法和学习机制3个方面针对数据驱动技术在电网暂态稳定评估中的应用研究工作进展以及关键技术进行综述,分析不同模型和方法在解决电力系统暂态稳定评估的数据拟合和泛化能力的优势和不足。最后,结合高比例新能源电力系统暂态稳定评估新特点和当前人工智能技术的发展,从数据、模型和应用3个方面对电力系统暂态稳定评估技术的研究方向进行展望,为电网暂态稳定评估数字化和智能化提供技术参考。
The rapid and accurate identification of the transient stability status of power systems is a crucial prerequisite for ensuring the safe and stable operation of large power grids.Compared with traditional physical analysis methods,data-driven transient stability assessment technology for power systems has significant advantages in solving complex nonlinear mapping and rapid evaluation,and has become an important direction in current research on transient stability assessment of power systems.This paper establishes the basic architecture of data-driven transient stability assessment technology based on the demand scenarios of power system transient stability assessment and the general intelligent application framework,and analyzes the functions of each process link in a data-driven context from the aspects of offline training,online application and feedback update.Furthermore,focusing on data enhancement,machine learning algorithms,and learning mechanisms,this paper reviews the progress of application research work and key technologies of data-driven technology in power grid transient stability assessment,and analyzes the advantages and disadvantages of different models and methods in solving the fitting and generalization capabilities of power system transient stability assessment models.Lastly,in light of the new characteristics of transient stability assessment for high proportion renewable power systems and the ongoing advancements in artificial intelligence technology,this paper anticipates the future research direction of power system transient stability assessment technology from three perspectives:data,model,and application,aiming to provide technical reference for the digitization and intelligent of power grid transient stability assessment.
作者
范士雄
赵泽宁
郭剑波
马士聪
王铁柱
李东琦
FAN Shixiong;ZHAO Zening;GUO Jianbo;MA Shicong;WANG Tiezhu;LI Dongqi(China Electric Power Research Institute,Haidian District,Beijing 100192,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2024年第9期3408-3428,I0006,共22页
Proceedings of the CSEE
基金
中国电科院青年科学家项目(52420023000Z)。
关键词
电力系统
暂态稳定评估
人工智能
数据驱动
主动学习
迁移学习
power system
transient stability assessment
artificial intelligence
data-driven
active learning
transfer learning