Reliability Assessment of Time-Dependent Systems via Sequential Cross-Entropy Monte Carlo Simulation

Gonzalez-Fernandez, Reinaldo A.; Silva, Armando M. Leite da

doi:10.1109/tpwrs.2011.2112785

Cited by 91 publications

(43 citation statements)

References 17 publications

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“…If the parameters l (n+1) b , j (n+1) b and t (n+1) b are found to be zero or infinite, then they will be left unchanged as their last iteration solution in a (n) CE . Sequentially, a (n) CE is further smoothly updated by (17). If t ′ n .…”

Section: Procedures Of Adaptive Sequential Importance Samplingmentioning

confidence: 99%

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Adaptive sequential importance sampling technique for short‐term composite power system adequacy evaluation

Wang

Guo

2014

IET Generation, Transmission & Distribution

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The well-known sequential Monte Carlo simulation is a powerful tool to analyse short-term reliability of complicated composite power system. However, the corresponding computational burden is tremendous when applied to a highly reliable system. Aiming at improving the simulation efficiency, an adaptive importance sampling technology is proposed in this study. The proposed method, on the basis of component state duration sampling technique combined with cross-entropy method, is able to provide reliability evaluation of highly reliable non-homogeneous Markov system. The Weibull and lognormal distributions are considered to describe repair time of individual components comprising a system. Through the case studies conducted on a reinforced Roy Billinton reliability test system, it is validated that the proposed method is effective and of high efficiency and the efficiency gain against the crude sequential Monte Carlo simulation is robust to variation of load level and lead timescale. The proposed method is useful and efficient to timely monitor the system operating pressure under different lead timescales.

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Section: Procedures Of Adaptive Sequential Importance Samplingmentioning

confidence: 99%

“…If t ′ n . T , n = :n + 1 and return to Step ii; otherwise, continue (17) Step iv: Independently simulate a set (S ′ ) of system state transition paths via a similar procedure as the CSDS with a (n) CE , except that in Step 4, S(Γ i ) is further multiplied by…”

Section: Procedures Of Adaptive Sequential Importance Samplingmentioning

confidence: 99%

Adaptive sequential importance sampling technique for short‐term composite power system adequacy evaluation

Wang

Guo

2014

IET Generation, Transmission & Distribution

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“…Recently, new derivatives of importance sampling method based on cross-entropy theory [12] are gaining ground in the area of power system adequacy reliability evaluations [13][14][15][16][17][18], and it has been found that simulation burdens, compared with conventional Monte Carlo methods in long-term planning applications, is greatly reduced both for composite system adequacy [16] and generating capacity evaluation [13][14][15]. Moreover, in the aspect of power system probabilistic short-term adequacy evaluation, two sequential importance sampling methods based on the cross-entropy theory [17,18] are developed to improve conventional sequential Monte Carlo methods.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic spinning reserve adequacy evaluation for generating systems using an Markov chain Monte Carlo‐integrated cross‐entropy method

Wang

2015

IET Generation, Transmission & Distribution

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Spinning reserve plays an important role in balancing generation and demand mismatch within a short time interval. Probabilistic spinning reserve adequacy evaluation (PSRAE) is useful to aid operators in monitoring the adequacy of system upward spinning reserve exposed to unforeseen disturbances and making risk-adverse decisions for unit dispatching. As a short time period, for example, 30 s-15 min, is usually considered in PSRAE, the generating system in question is commonly of high reliability, which renders naive non-sequential Monte Carlo methods inefficient. In this study, the high reliability phenomenon of generating systems is explained through a toy case study, thereafter, aiming to develop an efficient method for PSRAE, a simulation method based on the cross-entropy which has been widely applied in many areas to improve naive Monte Carlo methods for tackling rare-event simulation issues, integrated with Markov chain Monte Carlo is proposed. The indices of loss of load probability and expected demand not supplied are used to quantify the spinning reserve inadequacy risk. Through case studies conducted on the RTS-79, usefulness of the PSRAE is discussed and the proposed method is proven to be superior to the classical cross-entropy method and thus could be taken as a candidate tool for the PSRAE to practical systems.

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“…Due to its large computational burden in recent years, many efforts have been made to enhance MCS in order to improve its computational efficiency. Such efforts are pseudochronological MCS [8], [9], Cross Entropy (CE) [10], [11], Quasi-sequential, Quasi-CE [12]and Latin Hypercube Sampling (LHS) [13], which are combined with conventional MCS with the view to accelerate its CPU time. The last five years Population Intelligent Search (PIS) have been applied to the probabilistic reliability analysis of power systems.…”

Section: Introductionmentioning

confidence: 99%

MOPSO using probabilistic and deterministic criteria based on OHL's thermal ratings

Kapetanaki

Kopsidas

2014

2014 Power Systems Computation Conference

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Abstract-A Population Intelligent (PI) methodology calledParticle Swarm Optimization has recently been applied to power system networks with the view to minimize the computational burden of Monte Carlo Simulation in the reliability domain. This paper presents a novel Multi Objective Particle Swarm optimization (MOPSO) methodology which adapts traditional binary PSO to multi objective PSO and intelligently prunes the state space by using the thermal capacity of transmission lines derived from the more detailed modelling of OHLs. For the implementation of the algorithm, deterministic metrics are used to evaluate the loading of the lines with the view to further enhance the efficiency of the proposed method. The IEEE 24-bus RTS is used under different case studies to validate that the filtering based methodology achieves computational effectiveness as well as improves network performance indices.

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Reliability Assessment of Time-Dependent Systems via Sequential Cross-Entropy Monte Carlo Simulation

Cited by 91 publications

References 17 publications

Adaptive sequential importance sampling technique for short‐term composite power system adequacy evaluation

Adaptive sequential importance sampling technique for short‐term composite power system adequacy evaluation

Probabilistic spinning reserve adequacy evaluation for generating systems using an Markov chain Monte Carlo‐integrated cross‐entropy method

MOPSO using probabilistic and deterministic criteria based on OHL's thermal ratings

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