The Experts below are selected from a list of 345 Experts worldwide ranked by ideXlab platform

Zhen Gao - One of the best experts on this subject based on the ideXlab platform.

  • Long-Term analysis of gear loads in fixed offshore wind turbines considering ultimate operational loadings
    Energy Procedia, 2013
    Co-Authors: Amir Rasekhi Nejad, Zhen Gao, Torgeir Moan
    Abstract:

    Abstract The long-Term extreme value analysis of gear transmitted load due to the main shaft torque is presented. Two methods, the multibody simulations (MBS) and a simplified method, are demonstrated for the gear transmitted load calculation. The simplified method is verified by the MBS results. The long-Term extreme value of the gear transmitted load for wind speeds from the cut-in to the cut-out values is calculated by the simplified method from the long-Term Distribution of the main shaft torque. Three statistical methods for long-Term extreme value analysis of the main shaft torque in the offshore wind turbines are presented. They are then used to predict the extreme value of the gear transmitted load. An alternative approach, the design state or the environmental contour method is proposed and verified by the full long-Term results. The methods are exemplified by a 5 MW gearbox case study. The results of this paper are the basis for further work in Ultimate Limit State (ULS) gear design.

  • statistical uncertainty analysis in the long Term Distribution of wind and wave induced hot spot stress for fatigue design of jacket wind turbine based on time domain simulations
    ASME 2011 30th International Conference on Ocean Offshore and Arctic Engineering, 2011
    Co-Authors: Wenbin Dong, Torgeir Moan, Zhen Gao
    Abstract:

    The statistical uncertainty of the long-Term Distribution of wind- and wave-induced hot-spot stress ranges in multi-planar tubular joints of a fixed jacket offshore wind turbine designed for a North Sea site in a water depth of 70m has been assessed in this paper. The dynamic response of the jacket support structure due to wind and wave loads is calculated using a decoupled procedure. Hot-spot stresses at failure-critical locations of each reference brace for 4 different tubular joints (DK, DKT, X-type) are derived by summation of the single stress components from axial, in-plane and out-plane action. The effects of planar and non-planar braces are also considered. A two-parameter Weibull function is used to fit the long-Term statistical Distribution of hot-spot stress ranges by combination of time domain simulation for representative environmental conditions (wind / sea states) in operational condition of the wind turbine. The statistical uncertainty of the Weibull Distribution of hot-spot stress ranges and the two parameters defining the Weibull Distribution is assessed, based on 20 simulations for each representative environmental condition. The contributions to the uncertainty from wind loads and wave loads are analyzed by considering 3 different load cases: wind loads only, wave loads only and combination of wind and wave loads. The sensitivity of the long-Term Distribution of hot-spot stress ranges due to their stress components is also assessed.Copyright © 2011 by ASME

Torgeir Moan - One of the best experts on this subject based on the ideXlab platform.

  • Long-Term analysis of gear loads in fixed offshore wind turbines considering ultimate operational loadings
    Energy Procedia, 2013
    Co-Authors: Amir Rasekhi Nejad, Zhen Gao, Torgeir Moan
    Abstract:

    Abstract The long-Term extreme value analysis of gear transmitted load due to the main shaft torque is presented. Two methods, the multibody simulations (MBS) and a simplified method, are demonstrated for the gear transmitted load calculation. The simplified method is verified by the MBS results. The long-Term extreme value of the gear transmitted load for wind speeds from the cut-in to the cut-out values is calculated by the simplified method from the long-Term Distribution of the main shaft torque. Three statistical methods for long-Term extreme value analysis of the main shaft torque in the offshore wind turbines are presented. They are then used to predict the extreme value of the gear transmitted load. An alternative approach, the design state or the environmental contour method is proposed and verified by the full long-Term results. The methods are exemplified by a 5 MW gearbox case study. The results of this paper are the basis for further work in Ultimate Limit State (ULS) gear design.

  • statistical uncertainty analysis in the long Term Distribution of wind and wave induced hot spot stress for fatigue design of jacket wind turbine based on time domain simulations
    ASME 2011 30th International Conference on Ocean Offshore and Arctic Engineering, 2011
    Co-Authors: Wenbin Dong, Torgeir Moan, Zhen Gao
    Abstract:

    The statistical uncertainty of the long-Term Distribution of wind- and wave-induced hot-spot stress ranges in multi-planar tubular joints of a fixed jacket offshore wind turbine designed for a North Sea site in a water depth of 70m has been assessed in this paper. The dynamic response of the jacket support structure due to wind and wave loads is calculated using a decoupled procedure. Hot-spot stresses at failure-critical locations of each reference brace for 4 different tubular joints (DK, DKT, X-type) are derived by summation of the single stress components from axial, in-plane and out-plane action. The effects of planar and non-planar braces are also considered. A two-parameter Weibull function is used to fit the long-Term statistical Distribution of hot-spot stress ranges by combination of time domain simulation for representative environmental conditions (wind / sea states) in operational condition of the wind turbine. The statistical uncertainty of the Weibull Distribution of hot-spot stress ranges and the two parameters defining the Weibull Distribution is assessed, based on 20 simulations for each representative environmental condition. The contributions to the uncertainty from wind loads and wave loads are analyzed by considering 3 different load cases: wind loads only, wave loads only and combination of wind and wave loads. The sensitivity of the long-Term Distribution of hot-spot stress ranges due to their stress components is also assessed.Copyright © 2011 by ASME

  • Extreme response of a flexible riser system using a complete nonlinear long-Term approach
    1993
    Co-Authors: Knut-aril Farnes, Torgeir Moan
    Abstract:

    A procedure for a complete long-Term analysis is outlined. Special considerations that should be taken for flexible riser systems, selection of probability models for the short-Term Distribution and fitting procedures are discussed. The suggested probability models and fitting procedures are evaluated for responses in a riser with a steep wave configuration. The selected probability models and fitting procedures are used in a long-Term analysis for the riser system.

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

  • turning from tf idf to tf igm for Term weighting in text classification
    Expert Systems With Applications, 2016
    Co-Authors: Kewen Chen, Zuping Zhang, Jun Long, Hao Zhang
    Abstract:

    A new supervised Term weighting scheme called TF-IGM is proposed.It adopts a new statistical model to measure a Term's class distinguishing power.It makes full use of the fine-grained Term Distribution across different classes.It is adaptive to different text datasets by providing options or parameters.It outperforms TF-IDF and state-of-the-art supervised Term weighting schemes. Massive textual data management and mining usually rely on automatic text classification technology. Term weighting is a basic problem in text classification and directly affects the classification accuracy. Since the traditional TF-IDF (Term frequency & inverse document frequency) is not fully effective for text classification, various alternatives have been proposed by researchers. In this paper we make comparative studies on different Term weighting schemes and propose a new Term weighting scheme, TF-IGM (Term frequency & inverse gravity moment), as well as its variants. TF-IGM incorporates a new statistical model to precisely measure the class distinguishing power of a Term. Particularly, it makes full use of the fine-grained Term Distribution across different classes of text. The effectiveness of TF-IGM is validated by extensive experiments of text classification using SVM (support vector machine) and kNN (k nearest neighbors) classifiers on three commonly used corpora. The experimental results show that TF-IGM outperforms the famous TF-IDF and the state-of-the-art supervised Term weighting schemes. In addition, some new findings different from previous studies are obtained and analyzed in depth in the paper.

Jose Roberto Sanches Mantovani - One of the best experts on this subject based on the ideXlab platform.

  • a stochastic mixed integer convex programming model for long Term Distribution system expansion planning considering greenhouse gas emission mitigation
    International Journal of Electrical Power & Energy Systems, 2019
    Co-Authors: Juan M Homeortiz, Ozy D Melgardominguez, Mahdi Pourakbarikasmaei, Jose Roberto Sanches Mantovani
    Abstract:

    Abstract This paper proposes a multistage convex Distribution system planning model to find the best reinforcement plan over a specified horizon. This strategy deTermines planning actions such as reinforcement of existing substations, conductor replacement of overloaded feeders, and siting and sizing of renewable and dispatchable distributed generation units. Besides, the proposed approach aims at mitigating the greenhouse gas emissions of electric power Distribution systems via a monetary form. Inherently, this problem is a non-convex optimization model that can be an obstacle to finding the optimal global solution. To remedy this issue, convex envelopes are used to recast the original problem into a mixed integer conic programming (MICP) model. The MICP model guarantees convergence to optimal global solution by using existing commercial solvers. Moreover, to address the prediction errors in wind output power and electricity demands, a two-stage stochastic MICP model is developed. To validate the proposed model, detail analysis is carried out over various case studies of a 34-node Distribution system under different conditions, while to show its potential and effectiveness a 135-node system with two substations is used. Numerical results confirm the effectiveness of the proposed planning scheme in obtaining an economic investment plan at the presence of several planning alternatives and to promote an environmentally committed electric power Distribution network.

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

  • a stochastic mixed integer convex programming model for long Term Distribution system expansion planning considering greenhouse gas emission mitigation
    International Journal of Electrical Power & Energy Systems, 2019
    Co-Authors: Juan M Homeortiz, Ozy D Melgardominguez, Mahdi Pourakbarikasmaei, Jose Roberto Sanches Mantovani
    Abstract:

    Abstract This paper proposes a multistage convex Distribution system planning model to find the best reinforcement plan over a specified horizon. This strategy deTermines planning actions such as reinforcement of existing substations, conductor replacement of overloaded feeders, and siting and sizing of renewable and dispatchable distributed generation units. Besides, the proposed approach aims at mitigating the greenhouse gas emissions of electric power Distribution systems via a monetary form. Inherently, this problem is a non-convex optimization model that can be an obstacle to finding the optimal global solution. To remedy this issue, convex envelopes are used to recast the original problem into a mixed integer conic programming (MICP) model. The MICP model guarantees convergence to optimal global solution by using existing commercial solvers. Moreover, to address the prediction errors in wind output power and electricity demands, a two-stage stochastic MICP model is developed. To validate the proposed model, detail analysis is carried out over various case studies of a 34-node Distribution system under different conditions, while to show its potential and effectiveness a 135-node system with two substations is used. Numerical results confirm the effectiveness of the proposed planning scheme in obtaining an economic investment plan at the presence of several planning alternatives and to promote an environmentally committed electric power Distribution network.