Prediction of dynamic voltage stability status based on Hopf and limit induced bifurcations using extreme learning machine

Abstract

Abstract Evaluation of voltage stability status considering its dynamic boundaries is a key issue for saving global stability of power systems. However, this evaluation is a computationally demanding task and its implementation is very hard (if not impossible) for on-line environments such as dispatching centers of power systems. In this paper, a new viewpoint for the problem based on modeling it as a forecast process is proposed, which can be implemented with a low computation burden for practical power systems. For this purpose, a voltage stability classification model considering Hopf and limit induced bifurcations is proposed and a new forecast strategy to predict voltage stability class label based on the proposed classification is suggested. The suggested forecast strategy is composed of an information theoretic feature selection technique, extreme learning machine (ELM) as the forecast engine and a line search procedure to fine-tune the settings. The effectiveness of the proposed classification model and forecast strategy is extensively illustrated on the New England 39-bus and IEEE 145-bus test systems.