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Optimization in Renewable Energy Systems, 2017 Reliability Evaluation Methods, power system reliability evaluation methods can be divided into two categories: (1) Analytical methods, and (2) simulation methods.
Org/10.3182/ FR-4014.00048, get rights and content transient stability synchronization structure-preserving power network model, view Abstract, copyright 2010 ifac.
They generally provide expectation indices in a relatively short computing time.Monte Carlo technique is the widely used simulation method which has two different categories 21: (1) Sequential, and (2) nonsequential.In this paper, a new methodology for network reduction is proposed and the results are compared with those from other methodologies.The most common evaluation techniques using a set of approximate equations are failure mode analysis and minimum cut set analysis.There are several methods to analytically evaluate reliability, including fault tree analysis, failure mode, effect, and criticality analysis, Markov processes, minimum cut-set method, and network reduction method.The dimensionality reduction resulting analysis can therefore lose some or much of its significance.In addition, the reliability evaluation may be a qualitative study, in which the main factors that impact system reliability can be determined and prioritized, or a quantitative study, where the reliability is assessed through different parameters and indices defined and calculated for the system.System planning on a large-scale electric power system is computationally challenging.Three common sampling approaches in MCS are: (1) State sampling approach; (2) system state transition sampling approach; and (3) state duration sampling approach.Simulation methods estimate the reliability indices by simulating the actual process and random behavior of the system.On the other hand, a sequential MCS can address the sequential operating conditions of the system, and may be used to include time correlated events and states such as output generation of renewable-based generating units, demand profile, and customer decisions, which is more applicable for.Therefore, using this method, a nonchronological state of the system is determined.This is particularly the case when complex systems and complex operating procedures have to be modeled.The use of simulation techniques is very important in the reliability evaluation of such situations.Analytical techniques represent the system by a mathematical model and evaluate the reliability indices from this model using direct numerical solutions.The MCS methods are classified based on the methods used for sampling.On the contrary, assumptions are frequently required in order to simplify the problem and produce an analytical model of the system.Published by Elsevier Ltd.In a nonsequential Monte Carlo simulation (MCS the samples are taken without considering the time dependency of the states or sequence of the events in the system.
Analytical approaches are based on assumptions concerning the statistical distributions of failure rate and repair times.