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Optimal Placement of Wind Turbines in Wind Farm Layout Using Particle Swarm Optimization

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Journal of Modern Power Systems and Clean Energy 2021年第2期9卷 367-375页

作者： Philip Asaah Lili Hao Jing Ji the College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 210008China

An optimal geographical location of wind turbines can ensure the optimum total energy output of a wind *** study introduces a new solution to the optimization of wind farm layout(WFLO)problem based on a three-step strategy and particle swarm optimization as the main *** proposed strategy is applied to a certain WFLO to generate highly efficient optimal output *** case scenarios are considered to formulate the non-wake and wake effects at various *** required wind turbine positions within the wind farm are determined by the particle swarm optimization *** rule of thumb,which determines the wind turbine spacing,is thoroughly *** MATLAB simulation results verify the proposed three-step ***,the results are compared with those of existing research works,and it shows that the proposed optimization strategy yields a better solution in terms of total output power generation and efficiency with a minimized objective *** efficiencies of the three case studies considered herein increase by 0.65%,1.95%,and 1.74%,***,the simulation results indicate that the proposed method is robust in WFLO design because it further minimizes the objective function.

关键词： jensen model particle swarm optimization wake effect wind farm layout(WFLO) wind turbine

Study on Maize-water model for Supplemental Irrigation in Loess Plateau

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Agricultural Science & Technology 2015年第5期16卷 1048-1052,1072页

作者： Xing LI Mangmang GOU Inner Mongolia Engineering Research Center for Water-saving Agriculture Inner Mongolia Normal University Institute of Water Conservancy Science Inner Mongolia

The Loess Plateau has a typical semi-arid climate, and the area suffers from very harsh ecological environment, severe soil erosion and water runoff, and uneven distributed precipitation. Due to the relatively low holding capacity, current rainwater-collecting and conservation facilities can only supplement a maximum of18 mm of water for crop production in each irrigation. In this study, mathematical models were constructed to identify the water requirement critical period of maize crop by evaluating response of each individual developmental stage to supplemental irrigation with harvested rainwater. In the transformed jensen model, ETmin/Eta was used as the index of relative evapotranspiration. The use of relative yield and relative crop evapotranspiration was able to eliminate influences from unintended environmental factors. A BP neural network crop-water model for extreme water deficit condition was constructed using the index of relative evapotranspiration as the input and the index of relative yield as the output after iterative training and adjustment of weight values. Comparison of measured maize yields to those predicted by the two models confirmed that the BP neural network crop-water model is more accurate than the transformed jensen model in predicting the sensitivity index to waterdeficit at various growth stages and maize yield when provided with supplemental irrigation with harvested rainwater.

关键词： BP neural network model of crop response to water The transformed jensen model Supplemental irrigation with harvested rainwater

Consistency of Water Production Function models of Two Crops

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Agricultural Science & Technology 2016年第1期17卷 12-14页

作者：樊鹏王莹杨青伟杨喜会河南省科学院地理研究所河南郑州450052 兰考县畜牧局河南兰考475300

On basis of test information, the research performed analysis on water production function models of two crops, which indicated that water model of crops in whole growth stage and water model of crops indifferent growth stages have consistency as well as differences, providing references for optimization of irrigation water. Meanwhile, the research analyzed the deficiency of optimization on irrigation water for crops just by jensen model.

关键词： Water production function Parabolic model jensen model Consistency