基于XGBOOST-PSO提高受端电网电压暂态稳定的发电机无功优化方法

考虑发电机稳态输出无功对其支撑暂态电压恢复稳定能力的影响,基于机器学习研究优化发电机稳态输出无功提高电压对故障扰动保持暂态稳定能力的预防控制方法。该方法采用加权多二元表暂态稳定裕度指标量化母线暂态电压对不同预想故障扰动的综合稳定裕度,并基于指标排序确定稳定裕度薄弱母线。同时基于发电机无功调节对预想故障下各薄弱母线电压暂态稳定裕度综合作用灵敏度排序,选择作用灵敏发电机。在利用XGBoost建立根据系统稳态特征向量预测薄弱母线暂态电压稳定的分类模型基础上,以系统对预想故障扰动保持暂态电压稳定为约束、以减小发电机稳态无功调节对网损产生影响为目标,基于潮流计算寻优灵敏发电机稳态输出无功,以提高薄弱母线的暂态电压稳定性。最后采用雅湖直流接入江西电网的PSASP计算模型验证了所提方法的有效性。     

Human visual sensitivity based optimal local backlight dimming methodologies under different viewing conditions

An adaptive local backlight dimming algorithm is proposed considering human visual sensitivities under various viewing conditions. Local dimming amount is maximized based on contrast sensitivity function and local image characteristics to minimize perceived luminance and contrast losses maintaining image qualities. Average luminance, contrast and spatial frequency are evaluated as local image characteristics. As dimming based on local characteristics has blocking artifacts, a compensation method using edge luminance difference is proposed considering human responses. The proposed algorithms reduce backlight power consumption by 39.69% on average, with an average SSIM of 0.995. It can be increased by 5.66% with varying viewing conditions, resulting in a 45.35% power consumption reduction on average. The outperformances of the proposed algorithm in power consumption reduction and maintaining image quality are verified with various image quality metrics compared to recent dimming algorithms.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

一维水沙耦合模型参数敏感性分析——以2020年黄河下游洪水演进为例北大核心CSCD

一维水沙输移及河床冲淤模型中有较多理论上尚未解决的参数,而这些参数对一维模型精度的影响缺少定量分析。本文首先介绍了一维水沙耦合动力学模型,并将其应用于2020年汛期黄河下游的水沙过程模拟。计算结果表明:该模型能准确模拟水位和流量的变化,纳什效率系数基本大于0.85,均方根误差比相应的实测特征值小一个量级;计算沙峰虽然偏大,但计算的含沙量过程与实测过程整体符合,且模型计算的分河段冲淤量与断面地形法实测值较为接近。为分析模型中经验参数对模型计算结果的影响,以水文断面特征水沙参数的纳什效率系数作为目标函数,采用Sobol全局敏感性分析方法计算了各经验参数的敏感度指标。计算结果表明:糙率的总敏感度指标较高,是模型的高敏感参数;目标函数的选取对参数敏感度分析有一定影响,以含沙量纳什效率系数为目标函数时,挟沙力公式的系数及淤积时恢复饱和系数公式中指数的一阶敏感度和总敏感度指标较高;各参数组合对不同目标函数的影响差别较大,但对于同一目标函数存在影响作用相似的参数组合。全局敏感性分析方法刻画了模型参数的敏感性特征,有助于优化水沙数学模型参数取值。     

基于高斯混合模型及近似线性规划的风电系统校正控制方法

提出一种基于高斯混合模型及近似线性规划的风电系统校正控制方法。考虑到风电及负荷功率预测误差的非高斯特性及相关性,建立了描述风电及负荷功率联合概率分布的高斯混合模型。考虑切负荷、发电机功率调整以及统一潮流控制器等控制措施,以控制代价最小为目标函数,构建了计及电压、频率及线路潮流等静态安全约束的风电系统校正控制优化问题的数学模型。为求解含不确定变量的非线性优化问题,依据高斯混合模型的性质,在稳态运行点的邻域内将非线性约束近似转化为线性约束,用线性规划方法求解,并不断迭代直至收敛。最后,将该方法应用于修改后的IEEE 10机39节点算例系统验证其有效性,与场景法的对比结果表明该方法求解效率较高,且具有较好的经济性。     

适用于次同步振荡分析的直驱式风电场等值方法

为解决大规模直驱式风电场阻抗建模困难和数值仿真节点限制的问题,基于阻抗分析法的核心原理,以等值前、后直驱式风电场外阻抗特性相一致为目标,提出了适用于次同步振荡分析的直驱式风电场等值方法。该方法包括基于阻抗灵敏度分析的分群聚合和基于遗传算法的等值降阶2个步骤。分别以风电场含相同型号直驱风机和不同型号直驱风机为例,对等值前、后的直驱式风电场外阻抗特性进行对比分析。结果表明,所提方法能够实现大规模直驱式风电场模型的等值降阶,等值前、后的直驱式风电场外阻抗特性较为接近,其等值误差能够被量化。     

低温环境下FBG温度传感特性研究

在低温环境中,光纤光栅(Fiber Bragg Grating,FBG)材料的热膨胀系数和热光系数会发生改变,从而影响其温度传感特性。文章通过实验研究了裸光纤光栅传感器和黄铜管封装的光纤光栅传感器在低温下的温度传感特性。结果表明,在80~300 K温度范围,裸FBG温度传感器的灵敏度为6.43 pm/K,线性度为0.974,在80~230 K温度范围,温度与光纤光栅的中心波长呈现非线性关系;黄铜管封装的FBG温度传感器,在整个温度范围内灵敏度可达26 pm/K,线性度为0.996,较裸FBG温度传感器均有较大提升。对比实验表明,对光纤光栅进行封装,可以提高其温度灵敏度和线性度,改善温度传感特性。     

Strain Rate Effects on Dynamic Fractures in Fine‐grained Granitic Rock

The effect of dynamic strain rates on failure responses of a fine‐grained granitic rock is studied experimentally and theoretically. Theoretical investigation employs a model incorporating dynamic fracture criterion with damage mechanics theory. Experimental investigation is conducted using split Hopkinson pressure bar device. In order to investigate the effects of microstructure on dynamic fracture failure under different loading rates, fragment debris of each tested specimen is collected and analyzed. It is found through the debris analysis that the granitic rock breaks down into the fragment debris in grain size scales and the effect of strain rates on the formation of fragment debris appears to be related to the microstructure of the rock. It is also found that dynamic inertia induced by the dynamic loading can reduce the effect of friction confinement generated by the contact between the cylindrical specimen and two split Hopkinson pressure bars on the dynamic responses of the specimen. Theoretical evaluations agree with the corresponding experimental observations.     

Achieving robust design through statistical effect screening

This work proposes a method for statistical effect screening to identify design parameters of a numerical simulation that are influential to performance while simultaneously being robust to epistemic uncertainty introduced by calibration variables. Design parameters are controlled by the analyst, but the optimal design is often uncertain, while calibration variables are introduced by modeling choices. We argue that uncertainty introduced by design parameters and calibration variables should be treated differently, despite potential interactions between the two sets. Herein, a robustness criterion is embedded in our effect screening to guarantee the influence of design parameters, irrespective of values used for calibration variables. The Morris screening method is utilized to explore the design space, while robustness to uncertainty is quantified in the context of info‐gap decision theory. The proposed method is applied to the National Aeronautics and Space Administration Multidisciplinary Uncertainty Quantification Challenge Problem, which is a black‐box code for aeronautic flight guidance that requires 35 input parameters. The application demonstrates that a large number of variables can be handled without formulating simplifying assumptions about the potential coupling between calibration variables and design parameters. Because of the computational efficiency of the Morris screening method, we conclude that the analysis can be applied to even larger‐dimensional problems. (Approved for unlimited, public release on October 9, 2013, LA‐UR‐13‐27839, Unclassified.) Copyright © 2015 John Wiley & Sons, Ltd.