大扰动下电力系统频率最低点概率估计的解析方法初探OA

With the increase of the proportion of new energy capacity,the uncertainty of the frequency dynamic process of the power system after large disturbance is obviously enhanced,the deterministic analysis method has been unable to accurately and comprehensively characterize the frequency safety state and evolution situation of the system,and the time-domain simulation method also can not meet the needs of system analysis because of its low analysis efficiency.For this reason,the solution idea of extreme point analysis is selected,taking the frequency nadir after disturbance as the analysis object,an analytical method for probability estimation is proposed:Firstly,the statistical eigenvalues describing the uncertainty of the influencing factors of the frequency nadir are obtained by data-driven and model analysis,which provide input for probability estimation;Then,the probability density function(PDF)transformation of multivariate function is carried out based on the deterministic frequency nadir analytical formula:The PDF of the time of frequency nadir is obtained by using the central limit theorem,then divided into independent and explicit coupling,using the way of marginalizing the joint PDF of equal-dimensional variable groups and the way of variable processing the analytical formula after dimensional compression,to obtain the frequency nadir deviation;And finally,the PDF of the frequency nadir value is obtained by convolution of the PDFs of the frequency nadir deviation and the initial frequency.The numerical results show that it is necessary to use probabilistic analysis for the frequency dynamics of the disturbed system under the present operating conditions,and the proposed method can significantly improve the analysis efficiency while ensuring the estimation accuracy compared with the Monte Carlo method,among them,the method without considering the coupling relationship between variables has higher analysis efficiency and is suitable for the case of independent variables,while the method considering coupling relationship has higher analysis accuracy in the case of explicit coupling between variables.