Study on quick judgment of power system stability using improved k-NN and LASSO method

Abstract

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.

Keywords

• power grids
• regression analysis
• power system transient stability
• power system security
• power system control
• load dispatching
• power system stability
• power engineering computing
• quick judgment
• power system stability
• NN
• LASSO method
• dynamic security assessment
• operation systems
• calculation speed
• important performance indices
• nearest neighbour
• stability indicators
• critical clearing time
• simulation sample database
• historical online data
• logistic regression model
• absolute shrinkage
• selection operator
• stability features
• static quantities
• running state
• active power
• electric elements
• operation mode
• familiar samples
• chosen features
• online analysis system