Extenuating the parameters using HEC-HMS hydrological model for ungauged catchment in the central Omo-Gibe Basin of Ethiopia

Characteristics of ungauged catchments can be studied from the hydrological model parameters of gauged catchments. In this research, discharge prediction was carried out in ungauged catchments using HEC-HMS in the central Omo-Gibe basin. Linear regression, spatial proximity, area ratio, and sub-basin mean were amalgamated for regionalization. The regional model parameters of the gauged catchment and physical characteristics of ungauged catchments were collated together to develop the equations to predict discharge from ungauged catchments. From the sensitivity analysis, crop coefficient (CC), storage coefficient (R), constant rate (CR), and time of concentration (TC) are found to be more sensitive than others. The model efficiency was evaluated using Nash–Sutcliffe Efficiency (N_SE) which was greater than 0.75, varying between ?10% and +10% and the coefficient of determination (R²) was approximated to be 0.8 during the calibration and validation period. The model parameters in ungauged catchments were determined using the regional model (linear regression), sub-basin mean, area ratio, and spatial proximity methods, and the discharge was simulated using the HEC-HMS model. Linear regression was used in the prediction where p-value ≤ 0.1, determination coefficient (R²) = 0.91 for crop coefficient (CC) and 0.99 for maximum deficit (MD). Constant rate (CR), maximum storage (MS), initial storage (IS), storage coefficient (R), and time of concentration (TC) were obtained. The result is that an average of 30 m³/s and 15 m³/s as the maximum monthly simulated flow for ungauged sub-catchments, i.e. Denchiya and Mansa of the main river basin .     

Reducing wind power curtailment in China: comparing the roles of coal power flexibility and improved dispatch

Renewable energy curtailment is a critical issue in China, impeding the country’s transition to clean energy and its ability to meet its climate goals. This paper analyzes the impacts of more flexible coal-fired power generation and improved power dispatch towards reducing wind power curtailment. A unit commitment model for power dispatch is used to conduct the analysis, with different scenarios demonstrating the relative impacts of more flexible coal-fired generation and improved power dispatch. Overall, while we find both options are effective in reducing wind power curtailment, we find that improved power dispatch is more effective: (1) the effect of ramping down coal-fired generators to reduce wind power curtailment lessens as the minimum output of coal-fired generation is decreased; and (2) as a result, at higher wind capacity levels, wind curtailment is much more significantly reduced with improved power dispatch than with decreased minimum output of coal-fired generation.

Key policy insights

• China should emphasize both coal power flexibility and dispatch in its policies to minimize renewable power curtailment and promote clean energy transition.

• China should accelerate the process of implementing spot market and marginal cost-based economic dispatch, while making incremental improvements to the existing equal share dispatch in places not ready for spot market.

• A key step in improving of dispatch is incorporating renewable power forecasts into the unit commitment process and updating the daily unit commitment based on the latest forecast result.

• China should expand the coal power flexibility retrofit programme and promote the further development of the ancillary service market to encourage more flexibility from coal-fired generation.

     

广西气象数据中心机房供电系统设计与保障

针对供配电系统容易发生的安全隐患,优化设计配电系统结构、采用一体化运维监控平台和采用高质量、高可靠性的设备,消除单点故障瓶颈,确保在停电、设备故障、维修维护等各种情况下提供不中断或快速恢复供电,避免停电对业务正常运行造成影响,确保机房供电安全。     

基于小波长短期记忆网络的风电功率超短期概率预测

随着大规模的风电并网,风电所具有的间歇性与随机性对电力系统的稳定性产生了很大的影响,风电功率预测成为当前解决该问题重要的方式之一.本文利用长短期记忆（LSTM）网络良好的时序记忆特性,将小波分解技术与LSTM深度网络结合,提出基于小波长短期记忆网络的风电功率超短期概率预测模型.首先通过小波分解技术将原始时间序列进行平稳化处理,再建立各子序列样本的LSTM网络预测模型,借助最大似然估计法估计预测误差的高斯分布函数,最终实现对未来4 h时刻的风电功率概率区间预测.最后,采用中国东北某风电场数据对所提方法进行算例分析,结果表明,将小波分解与深度学习方法结合可以较好地提高预测的精度,提高概率预测的区间可靠性.     

基于Hv方法的三轴液压振动系统频响函数估计的研究

三轴液压振动系统是实验室内模拟振动环境的主要设备,其主要功能是精确地复现给定的功率谱和时间历程。振动控制技术是实现这一目标的关键技术,它以频响函数估计为基础。频响函数估计的精度影响控制过程的快速性和稳定性。本文对几种典型的频响函数估计方法进行了分析,针对传统方法精度低且受到矩阵病态条件约束等缺点,应用特征分析与摄动理论构造了一种多入多出的Hv方法,并且应用三轴液压振动系统的实测数据对其进行了检验。结果表明该方法是适用的。     

地下RC结构物地震响应特征土工离心试验的模拟

地下结构物抗震稳定性的研究具有重要实际意义。基于动态土工离心试验,应用有效应力法分析了不同水平地震作用下土层和地下RC结构物的最大水平位移、不同埋深的土层加速度和超孔隙水压力变化规律,以及结构物的破坏发展特征,并进一步与试验结果进行了相互验证和对比,取得了较好效果,为重要地下结构物的抗震设计提供了依据和参考。     

A new spatial coherence model and analytical coefficients for multi-support response spectrum combination

In this paper, a new spatial coherence model of seismic ground motions is proposed by a fi tting procedure. The analytical expressions of modal combination (correlation) coeffi cients of structural response are developed for multi-support seismic excitations. The coeffi cients from both the numerical integration and analytical solutions are compared to verify the accuracy of the solutions. It is shown that the analytical expressions of numerical modal combination coeffi cients are of high accuracy. The results of random responses of an example bridge show that the analytical modal combination coeff icients developed in this paper are accurate enough to meet the requirements needed in practice. In addition, the computational effi ciency of the analytical solutions of the modal combination coeff icients is demonstrated by the response computation of the example bridge. It is found that the time required for the structural response analysis by using the analytical modal combination coeffi cients is less than 1/20 of that using numerical integral methods.     

2020年新疆于田Ms6.4地震前热红外亮温异常研究

获取了2020年4-7月新疆于田Ms6.4地震区周边约400 km范围内连续的中国气象卫星FY-2G遥感热红外资料,选取北京时间凌晨1:00～5:00时的分钟值数据,经过小波变换和功率谱估计法进行处理,得到研究区地震前后相对功率谱异常演化过程,对比分析了 2019年相同频率同期的相对功率谱幅值,并讨论了异常时间演化过程...     

电源避雷器选型与安装应注意的事项

根据国际电工委员会与国家防雷规范的要求,确定电源避雷器选型与安装的最佳途径和应注意的事项。     

许昌市供电量与气象要素相关分析

利用20052006年许昌市逐日供电量和气温、降水、相对湿度等气象要素资料,分析了供电量和这些气象要素之间的相关关系,结果表明,温度为影响本地用电量的主要气象因子,特别是夏季用电量对气温变化的反应更加敏感.