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This paper presents the solution of Markov chain reliability models with a large state-space. To specify a system reliability model, we use our previously proposed methodology, which is based on the Stochastic Automata Networks formalism. We model parts of the system by arrowhead matrices with functional transition rates. As a result, the infinitesimal generator matrix of the reliability model has a distinctive structure. In this paper, we demonstrate that a block Gauss–Seidel method can be applied very efficiently to such a structure. The application of the proposed methodology is illustrated by an example of a standard 3/2 substation configuration. Even though its Markov chain reliability model has almost two million states, its steady-state probabilities can be estimated in just a few seconds of CPU time. [ABSTRACT FROM AUTHOR]

In this paper, stochastic automata networks (SANs) formalism to model reliability of power systems substations is applied. The proposed strategy allows reducing the size of state space of Markov chain model and simplifying system specification. Two case studies of standard configurations of substations are considered in detail. SAN models with different assumptions were created. SAN approach is compared with exact reliability calculation by using a minimal path set method. Modeling results showed that total independence of automata can be assumed for relatively small power systems substations with reliable equipment. In this case, the implementation of Markov chain model by a using SAN method is a relatively easy task. [ABSTRACT FROM AUTHOR]

The paper presents a novel method for statistical quantification of wind power variation based on a new concept of homogeneous generation periods (HGPs). It starts with the state-of-the-art review of approaches addressing the quantification of wind power variation. The suggested method is based on identification of (a) wind generation gradients (positive, negative, insignificant and ineligible) for the time steps and (b) HGPs (increasing, decreasing, constant and null-generation). In result, the method provides a statistical evaluation on variation of wind generation process in observation period in terms of statistical characteristics of HGPs. The validation of method is performed on a numerical set-up representing Lithuanian wind power cluster in a windy 13-day period. The validation results point to the effectiveness of the suggested method for the ex-post quantification of wind generation variation in a short-term time span. [ABSTRACT FROM AUTHOR]

Abstract: In this article we present a short-term (short-transient) dynamic stability assessment methodology for generators of a power system at characteristic/relevant operation modes. The suggested methodology combines a classical simulation methodology and Bayesian approach, the latter being widely applied in a variety of areas nowadays. The proposed methodology reduced the number of calculations and the respective cut in computational time, enabled obtaining quite precise results in case of the lack of real measurements. The idea was to replace the major portion of calculations with a mathematical model of the power system by estimation procedure. As for the specification of its features, the methodology takes into account characteristic operation modes of the system in annual cycle and uses dependency functions of the generators relating dynamic stability reference characteristics with the estimated characteristics. The estimated dynamic stability characteristics of the same generators were found with the Bayesian-approach-based estimation model which was trained by data sequence of true points from the reference characteristics. The performance of the methodology was examined by testing the dynamic stability of the generators of the power system and statistically analysing the test results. In summary, it can be reasonably argued that the best estimates were produced when the estimation model was trained with one dynamic stability reference characteristic and three true points of the characteristic for the considered generator. The verification of the suggested methodology for the aforementioned test system revealed that it reduces the computational time by several times, in comparison to “classical” methodology (based only on mathematical modelling of the power system). [Copyright &y& Elsevier]