中国电机工程学报2023，Vol.43Issue(23)：9046-9058,13.DOI:10.13334/j.0258-8013.pcsee.221721
面向电力系统智能分析的机器学习可解释性方法研究(二):电网稳定分析的物理内嵌式机器学习
Research on Interpretable Methods of Machine Learning Applied in Intelligent Analysis of Power System(Part Ⅱ):Physics-embedded Machine Learning for Power System Stability Analysis
摘要
Abstract
It is one of the important approaches to improve the interpretability and performance by integrating knowledge into machine learning models.In this paper,a new physics-embedded machine learning framework for power system stability analysis is proposed,in which the model is trained with the prior knowledge described by the differential-algebraic equations of the dynamic process of the power system fault.Compared to the traditional methods purely relying on the massive data,the physics-embedded machine learning model directly simulates the physical process.The physical equations contained in the data are used to guide the training procedure of the neural network and constrain the decision space of the machine learning.The dynamic curves of the fault produced by the model show explicit physical meaning,which makes the results more explainable.Meanwhile,the physics-embedded framework significantly reduces the demand of the samples,which provides new ways for the few shot learning and parameter identification when the machine learning model is applied to a real system.关键词
机器学习/可解释性/物理内嵌式机器学习/电力系统稳定分析/知识数据融合Key words
machine learning/interpretability/physics-embedded machine learning/stability analysis of power system/knowledge-data-combined method分类
信息技术与安全科学引用本文复制引用
乔骥,赵紫璇,王晓辉,史梦洁,蒲天骄..面向电力系统智能分析的机器学习可解释性方法研究(二):电网稳定分析的物理内嵌式机器学习[J].中国电机工程学报,2023,43(23):9046-9058,13.基金项目
国家重点研发计划项目(2021ZD0112700) （2021ZD0112700）
国家电网公司科技项目(SGBJDK00KJJS2250026) （SGBJDK00KJJS2250026）
国家自然科学基金项目(U2066213).National Key R&D Program of China(2021ZD0112700) （U2066213）
Science and Technology Project of State Grid Corporation of China(SGBJDK00KJJS2250026) （SGBJDK00KJJS2250026）
National Natural Science Foundation of China(U2066213). （U2066213）
