电力系统保护与控制
電力繫統保護與控製
전력계통보호여공제
POWER SYSTM PROTECTION AND CONTROL
2014年
6期
91-97
,共7页
王元凯%袁晓冬%柏晶晶%顾伟
王元凱%袁曉鼕%柏晶晶%顧偉
왕원개%원효동%백정정%고위
信息扩散%负荷综合建模%参数估计%概率密度%小样本
信息擴散%負荷綜閤建模%參數估計%概率密度%小樣本
신식확산%부하종합건모%삼수고계%개솔밀도%소양본
information spread%comprehensive load modeling%parameter estimation%probability density%small samples
为了满足电力系统先验仿真的要求,负荷模型需要概括不同时刻的负荷特性。针对负荷建模实测数据较少的情况,提出一种基于信息扩散估计的负荷综合建模方法。该方法首先利用负荷节点采集到的小样本单条扰动数据建立负荷模型,模型中仅辨识灵敏度大的参数以降低参数分散性的影响。然后基于信息扩散理论估计负荷模型参数的概率密度函数,利用K-S方法检验所得概率密度的正确性。最后根据此概率密度函数估计综合负荷模型的参数。该方法可以对现场实测的有限数据样本信息进行拓展,通过对样本信息的深度挖掘来估计模型参数的总体特征,使得所建负荷模型具有良好的泛化能力。
為瞭滿足電力繫統先驗倣真的要求,負荷模型需要概括不同時刻的負荷特性。針對負荷建模實測數據較少的情況,提齣一種基于信息擴散估計的負荷綜閤建模方法。該方法首先利用負荷節點採集到的小樣本單條擾動數據建立負荷模型,模型中僅辨識靈敏度大的參數以降低參數分散性的影響。然後基于信息擴散理論估計負荷模型參數的概率密度函數,利用K-S方法檢驗所得概率密度的正確性。最後根據此概率密度函數估計綜閤負荷模型的參數。該方法可以對現場實測的有限數據樣本信息進行拓展,通過對樣本信息的深度挖掘來估計模型參數的總體特徵,使得所建負荷模型具有良好的汎化能力。
위료만족전력계통선험방진적요구,부하모형수요개괄불동시각적부하특성。침대부하건모실측수거교소적정황,제출일충기우신식확산고계적부하종합건모방법。해방법수선이용부하절점채집도적소양본단조우동수거건립부하모형,모형중부변식령민도대적삼수이강저삼수분산성적영향。연후기우신식확산이론고계부하모형삼수적개솔밀도함수,이용K-S방법검험소득개솔밀도적정학성。최후근거차개솔밀도함수고계종합부하모형적삼수。해방법가이대현장실측적유한수거양본신식진행탁전,통과대양본신식적심도알굴래고계모형삼수적총체특정,사득소건부하모형구유량호적범화능력。
In order to meet the requirements of prior power system simulation, it is necessary to summarize the load characteristics at different time. Since the measured data are usually scarce, this paper proposes a new strategy to build the comprehensive load model. Firstly, single measured data are used to build the load model at every sampling time. In order to reduce the dispersibility of the parameters, only the parameters of high sensitivity are identified. And then the probability density functions of load model parameters are estimated based on the information spread principle. The correctness of the probability density functions is verified using K-S testing method. Finally, the parameters of the comprehensive load model are evaluated according to the probability density functions. This method can expand the limited measured data information. The general characteristics of the model parameters are estimated by deep analysis of the sampling information. A case study is presented to verify that the comprehensive load model established by the proposed mathods has good generalization ability.