The complexity of power system stability studies, especially voltage stability considering dynamic loading margin based on Hopf bifurcation and reactive loading margin based on limit-induced bifurcation, challenges the conventional methods the effectiveness of which highly depends on the operating point, particularly the load level. The occurrence of reactive limit causes changes in the stability boundaries of power systems and, in some cases, leads to instability. In the current study, an attempt was made to investigate dynamic loading margin (DLM) and reactive loading margin (RLM) as well as their effects on the occurrence of bifurcation points of power systems. Also, the states in which these bifurcations occur relative to each other are explained. Finally, in accordance with the results, a classification model for the identification of the operating point states of the power system is introduced. The purpose of presenting this classification is to determine the operational condition of power systems based on the mentioned boundaries without solving dynamic algebraic equations. To that end, an approach consisting of the phasor measurement unit (PMU) data acquisition, feature selection, and probabilistic neural network (PNN) is proposed to predict the power system voltage stability state. The effectiveness of the proposed strategy is shown on the New-England test system in normal and contingency states.
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