A Fast Product of Conditional Reduction Method for System Failure Probability Sensitivity Evaluation

Systemreliability sensitivity analysis becomes difficult due to involving the issues of the correlation between failure modes whether using analytic method or numerical simulation methods.A fast conditional reduction method based on conditional probability theory is proposed to solve the sensitivity analysis based on the approximate analytic method.The relevant concepts are introduced to characterize the correlation between failure modes by the reliability index and correlation coefficient,and conditional normal fractile the for the multi-dimensional conditional failure analysis is proposed based on the two-dimensional normal distribution function.Thus the calculation of system failure probability can be represented as a summation of conditional probability terms,which is convenient to be computed by iterative solving sequentially.Further the system sensitivity solution is transformed into the derivation process of the failure probability correlation coefficient of each failure mode.Numerical examples results show that it is feasible to apply the idea of failure mode relevancy to failure probability sensitivity analysis,and it can avoid multi-dimension integral calculation and reduce complexity and difficulty.Compared with the product of conditional marginalmethod,a wider value range of correlation coefficient for reliability analysis is confirmed and an acceptable accuracy can be obtained with less computational cost.

Dynamic Simulation of Distribution Networks Using Multi-stage Discontinuous Galerkin Method

Dynamic simulation plays a fundamental role in security evaluation of distribution networks(DNs).However,the strong stiffness and non-linearity of distributed generation(DG)models in DNs bring about burdensome computation and noteworthy instability on traditional methods which hampers the rapid response of simulation tool.Thus,a novel L-stable approximate analytical method with high accuracy is proposed to handle these problems.The method referred to as multistage discontinuous Galerkin method(MDGM),first derives approximate analytical solutions(AASs)of state variables which are explicit symbolic expressions concerning system states.Then,in each time window,it substitutes values for symbolic variables and trajectories of state variables are obtained subsequently.This paper applies MDGM to DG models to derive AASs.Local-truncation-error-based variable step size strategy is also developed to further improve simulation efficiency.In addition,this paper establishes detailed MDGM-based dynamic simulation procedure.From case studies on a numerical problem,a modified 33-bus system and a practical large-scale DN,it can be seen that proposed method demonstrates fast and dependable performance compared with the traditional trapezoidal method.