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  • Open Access


    Distributed Robust Optimal Dispatch for the Microgrid Considering Output Correlation between Wind and Photovoltaic

    Ming Li1,*, Cairen Furifu1, Chengyang Ge2, Yunping Zheng1, Shunfu Lin2, Ronghui Liu2

    Energy Engineering, Vol.120, No.8, pp. 1775-1801, 2023, DOI:10.32604/ee.2023.027215

    Abstract As an effective carrier of integrated clean energy, the microgrid has attracted wide attention. The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids. This paper proposes an optimization scheme based on the distributionally robust optimization (DRO) model for a microgrid considering solar-wind correlation. Firstly, scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function; then the generated scenario results are reduced by K-means clustering; finally, the probability confidence interval More >

  • Open Access


    Dynamic Fatigue Reliability Analysis of Transmission Gear Considering Failure Dependence

    Yonghua Li1,*, Xiaoning Bai2, Shanshan Shi1, Shenxuan Wang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1077-1092, 2022, DOI:10.32604/cmes.2022.018181

    Abstract Multiple failure modes and strength degradation are usually inherent in the gear transmission system, which brings new challenges for conducting fatigue reliability analysis and design. This paper proposes a novel dynamic fatigue reliability analysis method for failure dependence and strength degradation based on the combination of the Copula function and Gamma process. Firstly, the dynamic simulation model of the gear transmission system is established to obtain the dynamic stress-time history. The Gamma process is then used to describe the strength degradation to establish the dynamic stress-strength interference model. The marginal distribution functions of tooth contact More >

  • Open Access


    Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique

    Xueping Fan*, Guanghong Yang, Zhipeng Shang, Xiaoxiong Zhao, Yuefei Liu*

    Structural Durability & Health Monitoring, Vol.15, No.1, pp. 69-83, 2021, DOI:10.32604/sdhm.2021.011922

    Abstract This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder. Firstly, multivariate Bayesian dynamic linear model (MBDLM) considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections; secondly, with the proposed MBDLM, the dynamic correlation coefficients between any two performance functions can be predicted; finally, based on MBDLM and Gaussian copula technique, a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder, and the monitoring extreme deflection data from an actual bridge is provided to More >

  • Open Access


    Generalized Truncated Fréchet Generated Family Distributions and Their Applications

    Ramadan A. ZeinEldin1,2, Christophe Chesneau3,*, Farrukh Jamal4, Mohammed Elgarhy5, Abdullah M. Almarashi6, Sanaa Al-Marzouki6

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 791-819, 2021, DOI:10.32604/cmes.2021.012169

    Abstract Understanding a phenomenon from observed data requires contextual and efficient statistical models. Such models are based on probability distributions having sufficiently flexible statistical properties to adapt to a maximum of situations. Modern examples include the distributions of the truncated Fréchet generated family. In this paper, we go even further by introducing a more general family, based on a truncated version of the generalized Fréchet distribution. This generalization involves a new shape parameter modulating to the extreme some central and dispersion parameters, as well as the skewness and weight of the tails. We also investigate the More >

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