To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article com...To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article combines wind turbine monitoring data with numerical weather prediction(NWP)data to create a suitable wind power prediction framework for distributed grids.First,high-precision NWP of the turbine range is achieved using weather research and forecasting models(WRF),and Kriging interpolation locates predicted meteorological data at the turbine site.Then,a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve,and historical power is reconstructed using variational mode decomposition(VMD)filtering to form input variables in chronological order.Finally,input variables of a single turbine enter the temporal convolutional network(TCN)to complete initial feature extraction,and then integrate the outputs of all TCN layers using Long Short Term Memory Networks(LSTM)to obtain power prediction sequences for all turbine positions.The proposed method was tested on a wind farm connected to a distributed power grid,and the results showed it to be superior to existing typical methods.展开更多
利用观测的气象要素和大气污染物浓度资料,结合中尺度数值天气模式WRF(Weather Research and Forecasting Model),对2017年12月太原地区雾霾污染期间的天气条件进行了分析。多项研究模拟与观测结果对比发现,WRF模式能够较好地反映出某...利用观测的气象要素和大气污染物浓度资料,结合中尺度数值天气模式WRF(Weather Research and Forecasting Model),对2017年12月太原地区雾霾污染期间的天气条件进行了分析。多项研究模拟与观测结果对比发现,WRF模式能够较好地反映出某一地区地面和高空主要气象要素的时空分布。分析表明,此次雾霾主要是由于空气中悬浮的大气颗粒物浓度过高,在雾霾发生期间太原市近地面水平方向的小风速和静风现象偏多、垂直方向长时间存在逆温层削弱了大气的扩散能力,风向由西北风向偏南风的转变将太原周边地区污染物和水汽输送到太原,进而形成污染物的堆积,引起市区严重空气污染,造成此次大的雾霾。展开更多
基金funded by National Key Research and Development Program of China (2021YFB2601400)。
文摘To reduce carbon emissions,clean energy is being integrated into the power system.Wind power is connected to the grid in a distributed form,but its high variability poses a challenge to grid stability.This article combines wind turbine monitoring data with numerical weather prediction(NWP)data to create a suitable wind power prediction framework for distributed grids.First,high-precision NWP of the turbine range is achieved using weather research and forecasting models(WRF),and Kriging interpolation locates predicted meteorological data at the turbine site.Then,a preliminary predicted power series is obtained based on the fan’s wind speed-power conversion curve,and historical power is reconstructed using variational mode decomposition(VMD)filtering to form input variables in chronological order.Finally,input variables of a single turbine enter the temporal convolutional network(TCN)to complete initial feature extraction,and then integrate the outputs of all TCN layers using Long Short Term Memory Networks(LSTM)to obtain power prediction sequences for all turbine positions.The proposed method was tested on a wind farm connected to a distributed power grid,and the results showed it to be superior to existing typical methods.
文摘利用观测的气象要素和大气污染物浓度资料,结合中尺度数值天气模式WRF(Weather Research and Forecasting Model),对2017年12月太原地区雾霾污染期间的天气条件进行了分析。多项研究模拟与观测结果对比发现,WRF模式能够较好地反映出某一地区地面和高空主要气象要素的时空分布。分析表明,此次雾霾主要是由于空气中悬浮的大气颗粒物浓度过高,在雾霾发生期间太原市近地面水平方向的小风速和静风现象偏多、垂直方向长时间存在逆温层削弱了大气的扩散能力,风向由西北风向偏南风的转变将太原周边地区污染物和水汽输送到太原,进而形成污染物的堆积,引起市区严重空气污染,造成此次大的雾霾。
