An identification method for critical low frequency oscillation mode based on inter-area oscillation relevant generator contribution factor

Abstract

A novel identification method for critical oscillation mode is presented, which makes use of inter-area oscillation and relevant generator contribution factor. Firstly, the relevance between oscillation mode and electric parameters is analyzed. A state equation method is introduced to describe strongly relevant generator sets in multi-machine power system. Secondly, the oscillation assessing index is designed to calculate the risk of the rotor angle instability. And then, oscillation contribution factor, which concerns risk of inter-area oscillation relevant generators, is raised by means of synthesizing parameters like amplitude, damping ratio and attenuation obtained by Prony algorithm. The specified procedures of identifying critical oscillation mode are proposed based on the risk factor. Finally, simulation results of IEEE 10-machine 39-bus system and Sichuan power grid show that: the proposed method is able to identify critical oscillation mode and assess oscillation risk of the strongly relevant generators under different disturbance. Comparing the obtained result with that from energy class of oscillation modes verifies the feasibility and effectiveness of the proposed method.

A Study on Factors Affecting the Evaluation of the Quality of Party Building of General Party Branches in Higher Vocational Colleges and Universities

“…Since stepwise regression is an analytical method that introduces (or removes) variables into the regression equation one by one according to the significance of the influence of the explanatory variables on the explained variables, and it is a way to filter important variables in a multivariate situation, the use of stepwise regression analysis can better avoid multicollinearity between the explanatory variables (CuiXiang Liang, 2022) [6] . This method is an effective method of independent variable selection for regression modeling, which can screen the combination of independent variables that have the greatest impact on the dependent variable according to the size of the contribution of the independent variables to the variation of the dependent variable (Lin Bo et al, 2015) [7]7 . Therefore, this study used stepwise regression to analyze the factors affecting the quality of party building in the party branch.…”