基于多有源桥(multiple active bridge,MAB)的电力电子变压器(power electronic transformer,PET)具有“模块化,大规模,高复杂度”的特点,相比与其他基于双端口功率模块的PET拓扑,其电磁暂态加速仿真面临更大的困难。为提高仿真效率与CP...基于多有源桥(multiple active bridge,MAB)的电力电子变压器(power electronic transformer,PET)具有“模块化,大规模,高复杂度”的特点,相比与其他基于双端口功率模块的PET拓扑,其电磁暂态加速仿真面临更大的困难。为提高仿真效率与CPU利用率,文中提出一种适用于MAB型PET的并行等效建模方法。首先,根据“变压器端口解耦”的思路,建立PET串行等效模型。然后,利用所提等效方法的高度可并行性,给出等效模型多线程并行仿真框架,并进行并行算法评价与影响因素分析。通过PSCAD/EMTDC仿真验证,所提等效模型能够对详细模型进行多工况高度拟合,串行等效模型加速比可达2~3个数量级。在最优并行线程数下,并行等效模型可实现对串行模型2~3倍的二次加速。展开更多
Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction mod...Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.展开更多
文摘基于多有源桥(multiple active bridge,MAB)的电力电子变压器(power electronic transformer,PET)具有“模块化,大规模,高复杂度”的特点,相比与其他基于双端口功率模块的PET拓扑,其电磁暂态加速仿真面临更大的困难。为提高仿真效率与CPU利用率,文中提出一种适用于MAB型PET的并行等效建模方法。首先,根据“变压器端口解耦”的思路,建立PET串行等效模型。然后,利用所提等效方法的高度可并行性,给出等效模型多线程并行仿真框架,并进行并行算法评价与影响因素分析。通过PSCAD/EMTDC仿真验证,所提等效模型能够对详细模型进行多工况高度拟合,串行等效模型加速比可达2~3个数量级。在最优并行线程数下,并行等效模型可实现对串行模型2~3倍的二次加速。
文摘Developing a reliable weather forecasting model is a complicated task, as it requires heavy IT resources as well as heavy investments beyond the financial capabilities of most countries. In Lebanon, the prediction model used by the civil aviation weather service at Rafic Hariri International Airport in Beirut (BRHIA) is the ARPEGE model, (0.5) developed by the weather service in France. Unfortunately, forecasts provided by ARPEGE have been erroneous and biased by several factors such as the chaotic character of the physical modeling equations of some atmospheric phenomena (advection, convection, etc.) and the nature of the Lebanese topography. In this paper, we proposed the time series method ARIMA (Auto Regressive Integrated Moving Average) to forecast the minimum daily temperature and compared its result with ARPEGE. As a result, ARIMA method shows better mean accuracy (91%) over the numerical model ARPEGE (68%), for the prediction of five days in January 2017. Moreover, back to five months ago, in order to validate the accuracy of the proposed model, a simulation has been applied on the first five days of August 2016. Results have shown that the time series ARIMA method has offered better mean accuracy (98%) over the numerical model ARPEGE (89%) for the prediction of five days of August 2016. This paper discusses a multiprocessing approach applied to ARIMA in order to enhance the efficiency of ARIMA in terms of complexity and resources.
