基于任意概率分布建模与改进混沌多项式展开的概率潮流方法  

Probabilistic Power Flow Method Based on Arbitrary Probability Distribution Modeling and Improved Polynomial Chaos Expansion

作　　者：李可萌 王毅 闪鑫[1,2] 陆娟娟 LI Kemeng;WANG Yi;SHAN Xin;LU Juanjuan(NARI Group Corporation(State Grid Electric Power Research Institute),Nanjing 211106,China;NARI Technology Co.,Ltd.,Nanjing 211106,China)

机构地区：[1]南瑞集团有限公司(国网电力科学研究院有限公司),江苏省南京市211106 [2]国电南瑞科技股份有限公司,江苏省南京市211106

出　　处：《电力系统自动化》2024年第18期167-176,共10页Automation of Electric Power Systems

基　　金：国家重点研发计划资助项目(2022YFB2404200)。

摘　　要：针对目前概率潮流难以兼顾准确度和速度,且缺乏有效处理任意概率分布源荷数据的手段,提出了基于任意概率分布建模策略和改进混沌多项式展开的概率潮流方法。首先,将系统输入量拟合为概率分布类型库中的概率分布,基于赤池信息量准则选取最优分布,比较最优分布和非参核密度估计的似然估计值确定最终概率分布。其次,为提升最小角回归广义混沌多项式展开的准确度,提出应用伪谱法和矩匹配法获取备选点集合,并使用组合概率窗对其筛选获得最优备选点。然后,对原始概率空间执行拉丁超立方采样获取补充配置点,并与最优备选点结合后得到最终配置点。所提方法在IEEE 30、IEEE 118节点算例中得到验证,在相近耗时下,所提方法的准确度较不确定量化计算框架UQLab推荐算法有显著提升。In response to the current difficulty in balancing accuracy and speed in probabilistic power flow,as well as the lack of effective means to handle source and load data with arbitrary probability distributions,a probabilistic power flow method based on the arbitrary probability distribution modelling strategy and improved polynomial chaos expansion is proposed.Firstly,the system inputs are fitted to the probability distribution in the parameterized probability distribution type library.The optimal distribution is selected based on the Akaike information criterion,and the likelihood estimates of the optimal distribution and non-parametric kernel density estimation are compared to determine the final probability distribution.Secondly,to improve the accuracy of the generalized polynomial chaos expansions based on least angular regression,the pseudo spectral method and moment matching method are used to obtain a set of candidate points,and the combination probability window is used to filter them and obtain the optimal candidate points.Then,Latin hypercube sampling is performed on the original probability space to obtain supplementary configuration points,which are combined with the optimal candidate points to obtain the final configuration points.The proposed method has been validated in IEEE 30-bus and IEEE 118-bus cases,and its accuracy is significantly improved compared with the uncertainty quantification computing framework UQLab recommendation algorithm under similar time consumption.

关 键 词：概率潮流 概率分布建模 参数估计 混沌多项式展开 最小角回归 不确定性量化 

分 类 号：TM744[电气工程—电力系统及自动化]