Probabilistic design of power-system special stability controls

Abstract

A probabilistic approach to the design of power-system special stability controls is presented here. Using Monte-Carlo simulations, it takes into account all the potential causes of blackouts, slow and fast dynamics, and modeling uncertainties. A large number of scenarios are simulated in parallel by time-domain numerical integration, and the relevant parameters of the resulting system trajectories are stored in a database. Data-mining tools are used to identify the most important system weaknesses and possible improvements. The approach is tested on a large-scale study on the South-Eastern part of the extra-high-voltage system of Electricite & de France.1999 Elsevier Science¸td. All rights reserved.

A rare event approach to build security analysis tools when N − k (k >1) analyses are needed (as they are in large scale power systems)

“…The importance sampling distributions computed over the course of the cross-entropy algorithm algorithm could possibly also be used as classifiers for dangerous and non-dangerous events: indeed, they should ideally associate a low probability to non dangerous events and a high probability to dangerous ones. To this extent, the approach proposed has some similarities with the many works where classifiers for assessing the degree of severity of power system scenarios are built (see, e.g., [7], [17], [16]). …”

Multidisciplinary reliability design optimization under time-varying uncertainties

“…Currently, the methods considering time-varying uncertainties assume that strength and stress of mechanical parts follow certain probability distributions, respectively, [28][29][30][31] which cannot be used to evaluate dynamic evolution process of time-varying uncertainties. According to this, we attempt to propose a time-varying reliability prediction model based on stochastic differential equation theory which can calculate the reliability index at any time.…”

Consequence driven decomposition of large-scale power system security analysis

“…The importance sampling distributions computed over the course of the crossentropy algorithm could possibly also be used as classifiers for dangerous and non-dangerous contingencies: indeed, they should ideally associate a low probability to non-dangerous contingencies and a high probability to dangerous ones. To this extent, the proposed approach has some similarities with the many works where classifiers for assessing the degree of severity of power system scenarios are built (see, e.g., [17], [18], [19]). …”