Component Outage Estimation based on Support Vector Machine

Predicting power system component outages in response to an imminent hurricane plays a major role in preevent planning and post-event recovery of the power system. An exact prediction of components states, however, is a challenging task and cannot be easily performed. In this paper, a Support Vector Machine (SVM) based method is proposed to help estimate the components states in response to anticipated path and intensity of an imminent hurricane. Components states are categorized into three classes of damaged, operational, and uncertain. The damaged components along with the components in uncertain class are then considered in multiple contingency scenarios of a proposed Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the simultaneous outage of multiple components under an N-m-u reliability criterion. Experimental results on the IEEE 118-bus test system show the merits and the effectiveness of the proposed SVM classifier and the E-SCUC model in improving power system resilience in response to extreme events

Ruptures in the probability scale? Calculation of ruptures’ dimensions

The article raises the question of possible existence of ruptures, gaps in the probability scale which are caused by noises, uncertainties. A hypothesis of existence of such ruptures may be used to solve a number of problems of, e.g., utility theory in economics. The calculations give the dimensions of ruptures can be more than 1/3 of the standard deviation for the standard probability distributions.probability, uncertainty, utility, economics, decisions, risk,