Abstract
The failure probabilities of system components may vary with changes in the operating conditions. Performing a probabilistic risk assessment in real-time is challenging, since component failure probabilities are difficult to predict. Accordingly, this paper introduces a delayed semi-Markov process that incorporates real-time data from advanced sensors, as a means of efficiently calculating time-varying or condition-based failure probabilities. To demonstrate the feasibility of the procedure, a time-varying transformer outage model with numerical examples is presented. In the proposed technique, an analytic random model is developed to accommodate the impact of real-time dissolved gas analysis data, as well as other conditions pertaining to the failure probabilities of system components.
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