Real-time Static Security Situational Awareness of Power Systems Based on Relevance Vector Machine

Aim to the integration of cyber physical systems(CPS), a relevance vector machine(RVM) based data-driven method was proposed for real-time static security situational awareness. RVM is a general Bayesian probabilistic framework to learn the kernel-based classification model, in which a set of hyperparameters are imposed to the hierarchical priors over model parameters for obtaining the sparse solutions, and the Bernoulli distribution is incorporated to output a consistent estimation of the posterior probability. The operation conditions were firstly generated according to the dispatches of the day-ahead markets and the pre-fault feature sets with contingency class memberships were obtained. Then a distance-based Relief algorithm was employed for feature rank and selection. Finally, RVM learning for classification was applied for security recognition. A case studied in the IEEE30-bus system shows the proposed method can provide exceedingly sparse solutions, high accuracy and probabilistic outputs, further clarifying its superiority in security awareness.