The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform

D. Jolevski - One of the best experts on this subject based on the ideXlab platform.

  • transformer insulation coordination using volt time curve and Limit State Surface formulation
    International Journal of Electrical Power & Energy Systems, 2017
    Co-Authors: Petar Sarajcev, Damir Jakus, D. Jolevski
    Abstract:

    Abstract This paper presents a novel method for the power transformer insulation coordination, based on the risk of failure analysis due to lightning surges, that uses its insulation strength volt–time curve and a Limit–State Surface formulation. The Limit–State Surface is derived in a novel way, from the optimal number of systematic numerical simulations of transformer terminal overvoltages—emanating from station impinging lightning surges—while accounting for the transformer insulation volt–time curve and surge arresters protective characteristics and disposition. The proposed method further employs a State-of-the-art transmission line (TL) and substation equipment models for lightning-surge transient analysis, constructed in the EMTP software package. It also uses the electrogeometric model of lightning attachment to TLs, in order to estimate the expected number of direct lightning strikes, along with a bivariate statistical distribution of lightning currents. The main aspects of the proposed method are demonstrated by means of the computational example featuring an air-insulated substation power transformer lightning insulation coordination. Simulation results exhibit many benefits of the proposed method. Sensitivity analysis further reveals different influences that the various model parameters have on the transformer insulation coordination design.

  • Transformer insulation coordination using volt–time curve and Limit–State Surface formulation
    International Journal of Electrical Power & Energy Systems, 2017
    Co-Authors: Petar Sarajcev, Damir Jakus, D. Jolevski
    Abstract:

    Abstract This paper presents a novel method for the power transformer insulation coordination, based on the risk of failure analysis due to lightning surges, that uses its insulation strength volt–time curve and a Limit–State Surface formulation. The Limit–State Surface is derived in a novel way, from the optimal number of systematic numerical simulations of transformer terminal overvoltages—emanating from station impinging lightning surges—while accounting for the transformer insulation volt–time curve and surge arresters protective characteristics and disposition. The proposed method further employs a State-of-the-art transmission line (TL) and substation equipment models for lightning-surge transient analysis, constructed in the EMTP software package. It also uses the electrogeometric model of lightning attachment to TLs, in order to estimate the expected number of direct lightning strikes, along with a bivariate statistical distribution of lightning currents. The main aspects of the proposed method are demonstrated by means of the computational example featuring an air-insulated substation power transformer lightning insulation coordination. Simulation results exhibit many benefits of the proposed method. Sensitivity analysis further reveals different influences that the various model parameters have on the transformer insulation coordination design.

Petar Sarajcev - One of the best experts on this subject based on the ideXlab platform.

  • transformer insulation coordination using volt time curve and Limit State Surface formulation
    International Journal of Electrical Power & Energy Systems, 2017
    Co-Authors: Petar Sarajcev, Damir Jakus, D. Jolevski
    Abstract:

    Abstract This paper presents a novel method for the power transformer insulation coordination, based on the risk of failure analysis due to lightning surges, that uses its insulation strength volt–time curve and a Limit–State Surface formulation. The Limit–State Surface is derived in a novel way, from the optimal number of systematic numerical simulations of transformer terminal overvoltages—emanating from station impinging lightning surges—while accounting for the transformer insulation volt–time curve and surge arresters protective characteristics and disposition. The proposed method further employs a State-of-the-art transmission line (TL) and substation equipment models for lightning-surge transient analysis, constructed in the EMTP software package. It also uses the electrogeometric model of lightning attachment to TLs, in order to estimate the expected number of direct lightning strikes, along with a bivariate statistical distribution of lightning currents. The main aspects of the proposed method are demonstrated by means of the computational example featuring an air-insulated substation power transformer lightning insulation coordination. Simulation results exhibit many benefits of the proposed method. Sensitivity analysis further reveals different influences that the various model parameters have on the transformer insulation coordination design.

  • Transformer insulation coordination using volt–time curve and Limit–State Surface formulation
    International Journal of Electrical Power & Energy Systems, 2017
    Co-Authors: Petar Sarajcev, Damir Jakus, D. Jolevski
    Abstract:

    Abstract This paper presents a novel method for the power transformer insulation coordination, based on the risk of failure analysis due to lightning surges, that uses its insulation strength volt–time curve and a Limit–State Surface formulation. The Limit–State Surface is derived in a novel way, from the optimal number of systematic numerical simulations of transformer terminal overvoltages—emanating from station impinging lightning surges—while accounting for the transformer insulation volt–time curve and surge arresters protective characteristics and disposition. The proposed method further employs a State-of-the-art transmission line (TL) and substation equipment models for lightning-surge transient analysis, constructed in the EMTP software package. It also uses the electrogeometric model of lightning attachment to TLs, in order to estimate the expected number of direct lightning strikes, along with a bivariate statistical distribution of lightning currents. The main aspects of the proposed method are demonstrated by means of the computational example featuring an air-insulated substation power transformer lightning insulation coordination. Simulation results exhibit many benefits of the proposed method. Sensitivity analysis further reveals different influences that the various model parameters have on the transformer insulation coordination design.

Mi Xiao - One of the best experts on this subject based on the ideXlab platform.

  • A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties
    Reliability Engineering & System Safety, 2020
    Co-Authors: Jinhao Zhang, Liang Gao, Mi Xiao
    Abstract:

    Abstract This paper investigates the system reliability analysis under random and interval variables (SRA-RI). When performance functions in SRA-RI are replaced by surrogate models, it is found that the composite projection outlines on the composite Limit-State Surface should be well approximated so as to accurately assess system failure probability bounds. Then a composite-projection-outline-based active learning Kriging (CPOK) method is proposed in this paper. To refine the approximated composite projection outlines, three system learning functions are defined in CPOK for parallel, series and mixed systems, respectively. Based on these three functions, new points around the composite projection outlines are sequentially selected and used for the update of Kriging models. Meanwhile, prediction uncertainties of Kriging models are quantified and used for terminating the update of Kriging models. Finally, system failure probability bounds are evaluated by Monte Carlo simulation. The advantages of CPOK are validated by four numerical examples and a piezoelectric energy harvester example.

  • an efficient kriging based subset simulation method for hybrid reliability analysis under random and interval variables with small failure probability
    Structural and Multidisciplinary Optimization, 2019
    Co-Authors: Mi Xiao, Jinhao Zhang, Amin Toghi Eshghi
    Abstract:

    This paper proposes an efficient Kriging-based subset simulation (KSS) method for hybrid reliability analysis under random and interval variables (HRA-RI) with small failure probability. In this method, Kriging metamodel is employed to replace the true performance function, and it is smartly updated based on the samples in the first and last levels of subset simulation (SS). To achieve the smart update, a new update strategy is developed to search out samples located around the projection outlines on the Limit-State Surface. Meanwhile, the number of samples in each level of SS is adaptively adjusted according to the coefficients of variation of estimated failure probabilities. Besides, to quantify the Kriging metamodel uncertainty in the estimation of the upper and lower bounds of the small failure probability, two uncertainty functions are defined and the corresponding termination conditions are developed to control Kriging update. The performance of KSS is tested by four examples. Results indicate that KSS is accurate and efficient for HRA-RI with small failure probability.

  • A bounding-Limit-State-Surface-based active learning Kriging method for hybrid reliability analysis under random and probability-box variables
    Mechanical Systems and Signal Processing, 2019
    Co-Authors: Jinhao Zhang, Mi Xiao, Liang Gao, Sheng Chu
    Abstract:

    Abstract This paper presents a new method for efficient hybrid reliability analysis under both random and probability-box variables. Due to the existence of probability-box variables, the failure probability yielded by hybrid reliability analysis is an interval value. In practical engineering, numerical models are becoming more and more time-consuming, which promotes that metamodel-assisted reliability analysis methods gain considerable attention. The failure probability in hybrid reliability analysis under both random and probability-box variables can be calculated by transforming the original uncertainty space into the standard normal space. Then a Limit-State band with two bounding Limit-State Surfaces is generated in the standard normal space. In this paper, it is determined that the lower and upper bounds of failure probability can be accurately estimated based on a Kriging metamodel as it can well describe the two bounding Limit-State Surfaces. Then, a new active learning strategy based on bounding Limit-State Surface is proposed to sequentially update Kriging metamodel by adding new update points in the vicinity of the bounding Limit-State Surfaces into design of experiments. Meanwhile, two error measurement functions are presented to terminate the update process by calculating the metamodel error. Combining the bounding-Limit-State-Surface-based active learning Kriging with interval Monte Carlo simulation, a new method for hybrid reliability analysis under both random and probability-box variables is developed. In this method, the lower and upper bounds of failure probability are estimated by interval Monte Carlo simulation based on the built Kriging metamodel. The proposed method is tested by six examples. Its comparison with some existing reliability analysis methods is provided. The high accuracy and efficiency of the proposed method are validated by comparative results.

  • A new method for reliability analysis of structures with mixed random and convex variables
    Applied Mathematical Modelling, 2019
    Co-Authors: Jinhao Zhang, Mi Xiao, Liang Gao
    Abstract:

    Abstract This paper proposes a method combining projection-outline-based active learning strategy with Kriging metamodel for reliability analysis of structures with mixed random and convex variables. In this method, it is determined that the approximation accuracy of projection outlines on the Limit-State Surface is crucial for estimation of failure probability instead of the whole Limit-State Surface. To efficiently improve the approximation accuracy of projection outlines, a new projection-outline-based active learning strategy is developed to sequentially obtain update points located around the projection outlines. Taking into account the influence of metamodel uncertainty on the estimation of failure probability, a quantification function of metamodel uncertainty is developed and introduced in the stopping condition of Kriging metamodel update. Finally, Monte Carlo simulation is employed to calculate the failure probability based on the refined Kriging metamodel. Four examples including the Burro Creek Bridge and a piezoelectric energy harvester are tested to validate the performance of the proposed method. Results indicate that the proposed method is accurate and efficient for reliability analysis of structures with mixed random and convex variables.

  • a novel projection outline based active learning method and its combination with kriging metamodel for hybrid reliability analysis with random and interval variables
    Computer Methods in Applied Mechanics and Engineering, 2018
    Co-Authors: Jinhao Zhang, Mi Xiao, Liang Gao
    Abstract:

    Abstract This paper focuses on the hybrid reliability analysis with both random and interval variables (HRA-RI). It is determined that a metamodel only accurately approximating the projection outlines on the Limit-State Surface can precisely estimate the lower and upper bounds of failure probability in HRA-RI. According to this idea, a novel projection outline based active learning (POAL) method is proposed to sequentially update design of experiments (DoE). Then, a HRA-RI method combining POAL and Kriging metamodel (POAL–Kriging) is developed. In this method, Kriging metamodel is refined based on the update samples, which are sequentially chosen using POAL from the vicinity of the projection outlines on the Limit-State Surface. In the end, the lower and upper bounds of failure probability in HRA-RI are precisely estimated. Compared to the approximation of the whole Limit-State Surface, the proposed method only approximates the projection outlines on the Limit-State Surface, and therefore few DoE are needed to build a high quality metamodel. The accuracy, efficiency and robustness of the proposed method for HRA-RI are illustrated by four examples.

Damir Jakus - One of the best experts on this subject based on the ideXlab platform.

  • transformer insulation coordination using volt time curve and Limit State Surface formulation
    International Journal of Electrical Power & Energy Systems, 2017
    Co-Authors: Petar Sarajcev, Damir Jakus, D. Jolevski
    Abstract:

    Abstract This paper presents a novel method for the power transformer insulation coordination, based on the risk of failure analysis due to lightning surges, that uses its insulation strength volt–time curve and a Limit–State Surface formulation. The Limit–State Surface is derived in a novel way, from the optimal number of systematic numerical simulations of transformer terminal overvoltages—emanating from station impinging lightning surges—while accounting for the transformer insulation volt–time curve and surge arresters protective characteristics and disposition. The proposed method further employs a State-of-the-art transmission line (TL) and substation equipment models for lightning-surge transient analysis, constructed in the EMTP software package. It also uses the electrogeometric model of lightning attachment to TLs, in order to estimate the expected number of direct lightning strikes, along with a bivariate statistical distribution of lightning currents. The main aspects of the proposed method are demonstrated by means of the computational example featuring an air-insulated substation power transformer lightning insulation coordination. Simulation results exhibit many benefits of the proposed method. Sensitivity analysis further reveals different influences that the various model parameters have on the transformer insulation coordination design.

  • Transformer insulation coordination using volt–time curve and Limit–State Surface formulation
    International Journal of Electrical Power & Energy Systems, 2017
    Co-Authors: Petar Sarajcev, Damir Jakus, D. Jolevski
    Abstract:

    Abstract This paper presents a novel method for the power transformer insulation coordination, based on the risk of failure analysis due to lightning surges, that uses its insulation strength volt–time curve and a Limit–State Surface formulation. The Limit–State Surface is derived in a novel way, from the optimal number of systematic numerical simulations of transformer terminal overvoltages—emanating from station impinging lightning surges—while accounting for the transformer insulation volt–time curve and surge arresters protective characteristics and disposition. The proposed method further employs a State-of-the-art transmission line (TL) and substation equipment models for lightning-surge transient analysis, constructed in the EMTP software package. It also uses the electrogeometric model of lightning attachment to TLs, in order to estimate the expected number of direct lightning strikes, along with a bivariate statistical distribution of lightning currents. The main aspects of the proposed method are demonstrated by means of the computational example featuring an air-insulated substation power transformer lightning insulation coordination. Simulation results exhibit many benefits of the proposed method. Sensitivity analysis further reveals different influences that the various model parameters have on the transformer insulation coordination design.

Jinhao Zhang - One of the best experts on this subject based on the ideXlab platform.

  • A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties
    Reliability Engineering & System Safety, 2020
    Co-Authors: Jinhao Zhang, Liang Gao, Mi Xiao
    Abstract:

    Abstract This paper investigates the system reliability analysis under random and interval variables (SRA-RI). When performance functions in SRA-RI are replaced by surrogate models, it is found that the composite projection outlines on the composite Limit-State Surface should be well approximated so as to accurately assess system failure probability bounds. Then a composite-projection-outline-based active learning Kriging (CPOK) method is proposed in this paper. To refine the approximated composite projection outlines, three system learning functions are defined in CPOK for parallel, series and mixed systems, respectively. Based on these three functions, new points around the composite projection outlines are sequentially selected and used for the update of Kriging models. Meanwhile, prediction uncertainties of Kriging models are quantified and used for terminating the update of Kriging models. Finally, system failure probability bounds are evaluated by Monte Carlo simulation. The advantages of CPOK are validated by four numerical examples and a piezoelectric energy harvester example.

  • an efficient kriging based subset simulation method for hybrid reliability analysis under random and interval variables with small failure probability
    Structural and Multidisciplinary Optimization, 2019
    Co-Authors: Mi Xiao, Jinhao Zhang, Amin Toghi Eshghi
    Abstract:

    This paper proposes an efficient Kriging-based subset simulation (KSS) method for hybrid reliability analysis under random and interval variables (HRA-RI) with small failure probability. In this method, Kriging metamodel is employed to replace the true performance function, and it is smartly updated based on the samples in the first and last levels of subset simulation (SS). To achieve the smart update, a new update strategy is developed to search out samples located around the projection outlines on the Limit-State Surface. Meanwhile, the number of samples in each level of SS is adaptively adjusted according to the coefficients of variation of estimated failure probabilities. Besides, to quantify the Kriging metamodel uncertainty in the estimation of the upper and lower bounds of the small failure probability, two uncertainty functions are defined and the corresponding termination conditions are developed to control Kriging update. The performance of KSS is tested by four examples. Results indicate that KSS is accurate and efficient for HRA-RI with small failure probability.

  • A bounding-Limit-State-Surface-based active learning Kriging method for hybrid reliability analysis under random and probability-box variables
    Mechanical Systems and Signal Processing, 2019
    Co-Authors: Jinhao Zhang, Mi Xiao, Liang Gao, Sheng Chu
    Abstract:

    Abstract This paper presents a new method for efficient hybrid reliability analysis under both random and probability-box variables. Due to the existence of probability-box variables, the failure probability yielded by hybrid reliability analysis is an interval value. In practical engineering, numerical models are becoming more and more time-consuming, which promotes that metamodel-assisted reliability analysis methods gain considerable attention. The failure probability in hybrid reliability analysis under both random and probability-box variables can be calculated by transforming the original uncertainty space into the standard normal space. Then a Limit-State band with two bounding Limit-State Surfaces is generated in the standard normal space. In this paper, it is determined that the lower and upper bounds of failure probability can be accurately estimated based on a Kriging metamodel as it can well describe the two bounding Limit-State Surfaces. Then, a new active learning strategy based on bounding Limit-State Surface is proposed to sequentially update Kriging metamodel by adding new update points in the vicinity of the bounding Limit-State Surfaces into design of experiments. Meanwhile, two error measurement functions are presented to terminate the update process by calculating the metamodel error. Combining the bounding-Limit-State-Surface-based active learning Kriging with interval Monte Carlo simulation, a new method for hybrid reliability analysis under both random and probability-box variables is developed. In this method, the lower and upper bounds of failure probability are estimated by interval Monte Carlo simulation based on the built Kriging metamodel. The proposed method is tested by six examples. Its comparison with some existing reliability analysis methods is provided. The high accuracy and efficiency of the proposed method are validated by comparative results.

  • A new method for reliability analysis of structures with mixed random and convex variables
    Applied Mathematical Modelling, 2019
    Co-Authors: Jinhao Zhang, Mi Xiao, Liang Gao
    Abstract:

    Abstract This paper proposes a method combining projection-outline-based active learning strategy with Kriging metamodel for reliability analysis of structures with mixed random and convex variables. In this method, it is determined that the approximation accuracy of projection outlines on the Limit-State Surface is crucial for estimation of failure probability instead of the whole Limit-State Surface. To efficiently improve the approximation accuracy of projection outlines, a new projection-outline-based active learning strategy is developed to sequentially obtain update points located around the projection outlines. Taking into account the influence of metamodel uncertainty on the estimation of failure probability, a quantification function of metamodel uncertainty is developed and introduced in the stopping condition of Kriging metamodel update. Finally, Monte Carlo simulation is employed to calculate the failure probability based on the refined Kriging metamodel. Four examples including the Burro Creek Bridge and a piezoelectric energy harvester are tested to validate the performance of the proposed method. Results indicate that the proposed method is accurate and efficient for reliability analysis of structures with mixed random and convex variables.

  • a novel projection outline based active learning method and its combination with kriging metamodel for hybrid reliability analysis with random and interval variables
    Computer Methods in Applied Mechanics and Engineering, 2018
    Co-Authors: Jinhao Zhang, Mi Xiao, Liang Gao
    Abstract:

    Abstract This paper focuses on the hybrid reliability analysis with both random and interval variables (HRA-RI). It is determined that a metamodel only accurately approximating the projection outlines on the Limit-State Surface can precisely estimate the lower and upper bounds of failure probability in HRA-RI. According to this idea, a novel projection outline based active learning (POAL) method is proposed to sequentially update design of experiments (DoE). Then, a HRA-RI method combining POAL and Kriging metamodel (POAL–Kriging) is developed. In this method, Kriging metamodel is refined based on the update samples, which are sequentially chosen using POAL from the vicinity of the projection outlines on the Limit-State Surface. In the end, the lower and upper bounds of failure probability in HRA-RI are precisely estimated. Compared to the approximation of the whole Limit-State Surface, the proposed method only approximates the projection outlines on the Limit-State Surface, and therefore few DoE are needed to build a high quality metamodel. The accuracy, efficiency and robustness of the proposed method for HRA-RI are illustrated by four examples.