State Identification of Power System Based on Visual Geometric Group Network

Abstract

Accurate and timely identification of power system operation states are requisite for taking control strategies and ensuring power system security. With the high grid penetration of renewable energy generation and wide application of power electronic equipment, power system is becoming more complicated and facing more uncertainties. Traditional model based power system state identification methods are faced with challenges and data driven methods have great prospects. This paper proposes a new data-driven method for identifying power system operating states based on visual geometric group network VGG16 (16-layer Visual Geometry Group (VGG16). Firstly, the Lasso algorithm is used to select the operating state features of the power system to realize the features dimensionality reduction. Then, the weights of the loss functions are adjusted according to the difficulty of model for identifying power system states. Finally, the IEEE39 node system is used as an example, the effectiveness and robustness of the proposed method is verified.