Abstract
This paper presents a computationally efficient methodology for reliability evaluation of bulk power systems with wind power generation using small-signal stability analysis. The methodology is based on the application of Monte Carlo simulation to calculate reliability indices, where the sampled system states are evaluated by means of their small-signal stability condition. Therefore, the system small-signal stability is evaluated for scenarios that consider the failure of generation and transmission assets and the unavailability and power variations of wind farms. Unlike previous works, the proposed methodology does not consider the probability of small variations in system parameters, but instead the probability of occurrence of the scenarios themselves including the large and nonlinear variations resulting from network failures. This allows comparing different alternatives for the planning of large power systems, such as different PSS settings or system reinforcement solutions, in a more realistic approach. Additionally, the methodology uses a non-conventional partial eigenvalue solution focusing on a user-defined subset of poles of interest, efficient and practical for the small-signal analysis of large-scale power systems.
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