A Bayes Inference Method for Probabilistic Transient Stability Assessment of Power Systems
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A Bayesian inference approach is proposed for power systems transient stability assessment. Such assessment is crucial for modern power systems, which must often operate close to their stability limits, and are facing increasing levels of uncertainty. In particular, the estimation of the transient instability probability of a system is discussed in the paper, with some hints to interval estimation. First, the theoretical foundations of probabilistic stability assessment are reviewed according to already established models, which allow an analytical formulation of the instability probability in terms of the basic random variables affecting stability, such as the fault clearing time and the critical clearing time. Then, the novelty of the paper is illustrated, consisting in the above estimation, motivated by the observation that the parameters of the basic random variables (e.g. the expected values of the clearing times), being unknown in practice, have to be estimated. A large series of numerical simulations show that the proposed estimation method constitutes a very efficient method. Moreover, other numerous simulations have been performed for the purpose of a robustness analysis, showing that the methodology efficiency appears to hold also when departing from the assumptions made on prior parameter distributions and also on basic model distributions
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