A Method for Power System Static Stability Situation Assessment Based on Scale Invariant Feature Transform

Abstract

Power system static stability situation assessment is the core of power system security prevention and control. Most of traditional static stability assessment methods are focused on physical models and high-intensity simulations. These methods have a large amount of calculation and high dimensionality, and its online engineering applicability cannot be guaranteed. In order to better ensure the safe operation of power system, this paper proposes a static stability assessment method based on scale-invariant feature transformation(SIFT). This method directly extracts the association of holographic data under various static operating conditions of power system. Based on the operating feature it is improved for the generalized elasticity index and it is achieved for an accurate assessment of static stability situation. The simulation result of the New England 10-machine 39-bus system indicates that the improved grid generalized elasticity index has a higher slope and better engineering application value.