Incremental broad learning for real‐time updating of data‐driven power system dynamic security assessment models
Chao Ren, Yan Xu
Abstract
This work aims to real‐time update the data‐driven dynamic security assessment model for accuracy maintenance and improvement. Based on the random vector functional link neural network, an incremental broad learning model is developed as an alternative and much faster approach for deep learning. Besides, the proposed method can significantly reduce the computation burden caused by abundant model parameters of layers and filters. Three real‐time increment scenarios are achieved: enhancement hidden nodes, features, and training instances. Distinguished from existing methods, the proposed method can fast remodel in broad expansion without retraining the whole model, since it can only calculate the pseudoinverse of new state matrix without the computations of the whole output weights matrix. Numerical simulation results show that the proposed method can improve the accuracy step by step and update the model within a very short time at the online stage, verifying its real‐time computational efficiency and accuracy improvement.