%0 Electronic Article
%A Dongyu Shi
%A Fang Tian
%A Tie Li
%A Xianbo Meng
%A Junci Tang
%A Yanhao Huang
%A Dai Cui
%A Yushi Zhang
%A Hui Zeng
%K online analysis system
%K selection operator
%K dynamic security assessment
%K stability indicators
%K calculation speed
%K familiar samples
%K LASSO method
%K static quantities
%K operation systems
%K running state
%K historical online data
%K NN
%K electric elements
%K critical clearing time
%K stability features
%K operation mode
%K important performance indices
%K power system stability
%K active power
%K nearest neighbour
%K logistic regression model
%K absolute shrinkage
%K chosen features
%K simulation sample database
%K quick judgment
%X Dynamic security assessment is widely used in dispatching operation systems, and calculation speed is one of its most important performance indices. In this study, an improved k-nearest neighbour (k-NN) method is proposed aiming to predict the stability indicators of power system, for example, critical clearing time. The method is much faster than the simulation and suitable for online analysis. Firstly, a simulation sample database is constructed based on historical online data and a logistic regression model with least absolute shrinkage and selection operator is trained to pick the stability features, which are chosen from static quantities like running state and active power of electric elements. While a new operation mode needs to be evaluated, a weighted k-NN is implemented to obtain the most familiar samples in the database using the chosen features; the final result will be determined comprehensively by the familiar samples. The validity of the proposed method is verified by simulation using online data of State Grid Corp of China and different key faults. It is proved that the method meets the requirements for speed and accuracy of online analysis system.
%T Study on quick judgment of power system stability using improved *k-*NN and LASSO method
%B The Journal of Engineering
%D October 2018
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=b06rjqk0rshif.x-iet-live-01content/journals/10.1049/joe.2018.8359
%G EN