VIRTUAL LOAD DOMINANT PARAMETER IDENTIFICATION METHOD BASED ON INCREMENTAL LEARNING

A virtual load dominant parameter identification method based on incremental learning, comprising following steps of: (1) randomly selecting dominant parameters from a virtual load model for simulation; (2) establishing a deep learning neural network; (3) performing incremental learning on the neural network; and (4) performing fast online identification and cyclic training. According to the present invention, the feasibility of applying incremental learning to power system analysis is mainly described and the incremental learning is combined with load parameter identification, which improves training efficiency while ensuring identification accuracy, and prevents catastrophic forgetting while maintaining storage overhead, thus providing a new idea for processing training samples in parameter identification, as well as the technical support for online identification of dominant parameters of virtual load models. Based on the idea of continuous training and fast online identification, the convolution neural network is applied to the parameter identification of load models.