Online Static Voltage Stability Assessment Based on Constrained Voting Extreme Learning Machine

Rapid and accurate online static voltage stability assessment is important guarantee to ensure secure and stable operation of large-scale interconnected power grids. In allusion to such defects existing in traditional neural network learning model as many and diverse parameter invocation, long training time and large number of sample requirements, based on constrained voting extreme learning machine (abbr. CV-ELM) an online static voltage stability assessment model was proposed. Based on the sample differences among classes the difference vector set was constructed by CV-ELM to calculate the weights of the input layer to the hidden layer and the bias item of the hidden layer nodes, and the majority voting mechanism was led in to conduct the classification decision by ensemble learning. Furthermore, the network parameters could be determined by CV-ELM adaptively and in the aspects of the classification accuracy, robustness and generalization ability were better than those by traditional ELM. The effectiveness of the proposed model is proved by simulation results from the computing example based on New England 10-machine 39-bus system.