RT Journal Article
A1 Dongyu Shi
A1 Fang Tian
A1 Tie Li
A1 Xianbo Meng
A1 Junci Tang
A1 Yanhao Huang
A1 Dai Cui
A1 Yushi Zhang
A1 Hui Zeng

PB iet
T1 Study on quick judgment of power system stability using improved k-NN and LASSO method
JN The Journal of Engineering
VO 2019
IS 16
SP 686
OP 689
AB 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.
K1 online analysis system
K1 selection operator
K1 dynamic security assessment
K1 stability indicators
K1 calculation speed
K1 familiar samples
K1 LASSO method
K1 static quantities
K1 operation systems
K1 running state
K1 historical online data
K1 NN
K1 electric elements
K1 critical clearing time
K1 stability features
K1 operation mode
K1 important performance indices
K1 power system stability
K1 active power
K1 nearest neighbour
K1 logistic regression model
K1 absolute shrinkage
K1 chosen features
K1 simulation sample database
K1 quick judgment
DO https://doi.org/10.1049/joe.2018.8359
UL https://digital-library.theiet.org/;jsessionid=1hk81blmh6pa3.x-iet-live-01content/journals/10.1049/joe.2018.8359
LA English
SN
YR 2019
OL EN