Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ...Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.展开更多
In order to study the fatigue properties of rib-to-deck welded connection and rib-to-rib welded connection in orthotropic steel bridge decks,a multi-scale finite element model was set up to analyze the stress distribu...In order to study the fatigue properties of rib-to-deck welded connection and rib-to-rib welded connection in orthotropic steel bridge decks,a multi-scale finite element model was set up to analyze the stress distribution characteristics and the load test was conducted on the Taizhou Yangtze River Bridge.Comparing the vehicle test results with the muli-scale finite element model results to verify the accuracy of the finite element simulation for the stress response of two welded details.The results indicated that The stress at the rib-to-deck welded connection and the rib-to-rib welded connection are the bending stress and the membrane stress,respectively;the stress response of the two welded connection has strong local characteristics;the lateral stress influence line of the two welded connection is relatively short and the length of the lateral stress influence line is greatly affected by the longitudinal ribs;increasing the thickness of the roof and longitudinal ribs can reduce the stress response and improve the stress performance of the heavy lanes.For the two welded details,the fatigue damage increment of the ordinary lane is greater than the heavy lane.The thickened roof and longitudinal ribs at the position of the heavy lane still cannot balance the fatigue damage caused by the heavy truck.Therefore,it is necessary to strictly control the fatigue effect of overloaded vehicles on steel box girders.展开更多
In the realm of engineering practice,various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra.Thus,accurately predi...In the realm of engineering practice,various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra.Thus,accurately predicting the fatigue life of structures becomes notably arduous.This paper proposed an approach to predict the fatigue life of structure based on the optimized load spectra,which is accurately estimated by an efficient hinging hyperplane neural network(EHH-NN)model.The construction of the EHH-NN model includes initial network generation and parameter optimization.Through the combination of working conditions design,multi-body dynamics analysis and structural static mechanics analysis,the simulated load spectra of the structure are obtained.The simulated load spectra are taken as the input variables for the optimized EHH-NN model,while the measurement load spectra are used as the output variables.The prediction results of case structure indicate that the optimized EHH-NN model can achieve the high-accuracy load spectra,in comparison with support vector machine(SVM),random forest(RF)model and back propagation(BP)neural network.The error rate between the prediction values and the measurement values of the optimized EHH-NN model is 4.61%.In the Cauchy-Lorentz distribution,the absolute error data of 92%with EHH-NN model appear in the intermediate range of±1.65%.Also,the fatigue life analysis is performed for the case structure,based on the accurately predicted load spectra.The fatigue life of the case structure is calculated based on the comparison between the measured and predicted load spectra,with an accuracy of 93.56%.This research proposes the optimized EHH-NN model can more accurately reflect the measurement load spectra,enabling precise calculation of fatigue life.Additionally,the optimized EHH-NN model provides reliability assessment for industrial engineering equipment.展开更多
To fully explore the potential features contained in power load data,an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed.Firstly,a density peak f...To fully explore the potential features contained in power load data,an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed.Firstly,a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data,accurately dividing subsets of data into different categories.Secondly,introducing convolutional block attention mechanism into the bidirectional gated recurrent unit(BiGRU)structure significantly enhances its ability to extract key features.On this basis,in order to make the model more accurately adapt to the dynamic changes in power load data,subsets of different categories of data were used for BiGRU training based on attention mechanism,and extreme gradient boosting was selected as the meta model to effectively integrate multiple sets of historical training information.To further optimize the parameter configuration of the meta model,Bayesian optimization techniques are used to achieve automated adjustment of hyperparameters.Multiple sets of comparative experiments were designed,and the results showed that the average absolute error of the method in this paper was reduced by about 8.33%and 4.28%,respectively,compared with the single model and the combined model,and the determination coefficient reached the highest of 95.99,which proved that the proposed method has a better prediction effect.展开更多
The internal solitary wave(ISW)represents a frequent and severe oceanic dynamic phenomenon observed in the South China Sea,exposing marine structures to sudden loads.This paper examines the prediction model of interac...The internal solitary wave(ISW)represents a frequent and severe oceanic dynamic phenomenon observed in the South China Sea,exposing marine structures to sudden loads.This paper examines the prediction model of interaction loads between ISW and FPSO,accounting for varying attack angles and incorporating ISW theories.The research demonstrates that the horizontal and transverse forces on FPSO under internal solitary waves(ISWs)comprise wave pressure difference force and viscous force,while the vertical force primarily consists of vertical wave pressure difference force.The wave pressure difference force is determined using the Froude-Krylov equation.The viscous force is derived from the tangential particle velocity induced by ISW and the viscous coefficient.The viscous coefficient formula is obtained through regression analysis of experimental data with different ISW attack angles.The research reveals that the horizontal viscous coefficient C_(vx)decreases as Reynolds number(R_(e))increases,while the transverse viscous coefficient C_(vy)initially increases and subsequently decreases with the growth of the Keulegan-Carpenter number(KC).Moreover,changes in wave propagation direction significantly affect the extreme magnitudes of both horizontal and transverse forces,and simultaneously modify the transverse force orientation,while having minimal impact on the vertical force.Additionally,the forces increase with the ISW’s amplitude.For horizontal and transverse forces,a thinner upper fluid layer generates larger forces.Comparative analysis of experimental,numerical,and theoretical results indicates strong agreement between theoretical predictions and experimental and numerical outcomes.展开更多
针对非侵入式负荷分解方法负荷特征捕捉不足、负荷分解精度不够等问题,文章提出一种基于改进BERT(bidirectional encoder representations from transformers)模型的多头自注意力非侵入式负荷分解方法(frequency and temporal attention...针对非侵入式负荷分解方法负荷特征捕捉不足、负荷分解精度不够等问题,文章提出一种基于改进BERT(bidirectional encoder representations from transformers)模型的多头自注意力非侵入式负荷分解方法(frequency and temporal attention-BERT, FAT-BERT)。首先通过傅里叶变换将时域数据转换为频域数据,采用多尺度卷积全面捕捉负荷信号的时域和频域特征,从而增强模型对多样化负荷信号的表达能力;其次,在多头自注意力机制中引入频率注意力机制,从而增强模型对时序数据中频率成分的感知能力,进一步改善复杂负荷模式的表示,改进BERT模型中增加局部自注意力从而减少不必要的全局计算,提升模型的运行速度;接着将残差连接和正则化技术结合使模型在训练过程中更加稳定,并且能够更好地避免过拟合,最后在REDD和UK-DALE数据集上对提出的方法进行实验,实验结果验证了所提方法的有效性。展开更多
负荷预测是综合能源系统(integrated energy system,IES)高效运行的前提,面对综合能源系统多元负荷强耦合相关性、强随机性的特点,单一模型在运行负荷特征提取方面存在不足。为充分利用负荷间的相关性、降低负荷数据的非平稳性、弥补单...负荷预测是综合能源系统(integrated energy system,IES)高效运行的前提,面对综合能源系统多元负荷强耦合相关性、强随机性的特点,单一模型在运行负荷特征提取方面存在不足。为充分利用负荷间的相关性、降低负荷数据的非平稳性、弥补单一模型的不足,提出一种基于TCN-TPABiLSTM组合模型和多任务学习框架的IES多元负荷超短期协同预测方法。首先对负荷间耦合相关性、负荷时间相关性和负荷影响因素进行分析以构建模型输入,再通过变分模态分解将负荷数据分解为一定数量的模态以降低非平稳性,最后以TCN-TPA-BiLSTM组合模型作为多任务学习框架的共享层进行预测。通过实际数据进行验证和对比,结果表明该方法能够充分发挥模型各部分优势,相较于其他模型也获得了更优的结果。展开更多
在信息化蓬勃发展的今日,大量云计算资源的高效管理是运维领域的重要难题。准确的负载预测是应对这一难题的关键技术。针对该问题提出一种基于局部加权回归周期趋势分解算法(Seasonal and Trend decomposition using Loess,STL)、Holt-W...在信息化蓬勃发展的今日,大量云计算资源的高效管理是运维领域的重要难题。准确的负载预测是应对这一难题的关键技术。针对该问题提出一种基于局部加权回归周期趋势分解算法(Seasonal and Trend decomposition using Loess,STL)、Holt-Winters模型和深度自回归模型(DeepAR)的组合预测模型STL-DeepAR-HW。先采用快速傅里叶变换和自相关函数提取数据的周期性特征,以提取到的最优周期对数据做STL分解,将数据分解为趋势项、季节项和余项;并用DeepAR和Holt-Winters分别预测趋势项和季节项,最后组合得到预测结果。在公开数据集AzurePublicDataset上进行实验,结果表明,与Transformer、Stacked-LSTM以及Prophet等模型相比,该组合模型在负载预测中具有更高的准确性和适用性。展开更多
To achieve high parallel efficiency for the global MASNUM surface wave model, the algorithm of an irregular quasirectangular domain decomposition and related serializing of calculating points and data exchanging schem...To achieve high parallel efficiency for the global MASNUM surface wave model, the algorithm of an irregular quasirectangular domain decomposition and related serializing of calculating points and data exchanging schemes are developed and conducted, based on the environment of Message Passing Interface(MPI). The new parallel version of the surface wave model is tested for parallel computing on the platform of the Sunway BlueLight supercomputer in the National Supercomputing Center in Jinan. The testing involves four horizontal resolutions, which are 1°×1°,(1/2)°×(1/2)°,(1/4)°×(1/4)°, and(1/8)°×(1/8)°. These tests are performed without data Input/Output(IO) and the maximum amount of processors used in these tests reaches to 131072. The testing results show that the computing speeds of the model with different resolutions are all increased with the increasing of numbers of processors. When the number of processors is four times that of the base processor number, the parallel efficiencies of all resolutions are greater than 80%. When the number of processors is eight times that of the base processor number, the parallel efficiency of tests with resolutions of 1°×1°,(1/2)°×(1/2)° and(1/4)°×(1/4)° is greater than 80%, and it is 62% for the test with a resolution of(1/8)°×(1/8)° using 131072 processors, which is the nearly all processors of Sunway BlueLight. When the processor's number is 24 times that of the base processor number, the parallel efficiencies for tests with resolutions of 1°×1°,(1/2)°×(1/2)°, and(1/4)°×(1/4)° are 72%, 62%, and 38%, respectively. The speedup and parallel efficiency indicate that the irregular quasi-rectangular domain decomposition and serialization schemes lead to high parallel efficiency and good scalability for a global numerical wave model.展开更多
文摘Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.
基金This research has been supported by the National Natural Science Foundation of China(Grant No.51778135)the National Key R&D Program Foundation of China(Grant No.2017YFC0806001)+2 种基金the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20160207)Aeronautical Science Foundation of China(Grant No.20130969010)the Fundamental Research Funds for the Central Universities and Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant Nos.KYCX18_0113 and KYLX16_0253).
文摘In order to study the fatigue properties of rib-to-deck welded connection and rib-to-rib welded connection in orthotropic steel bridge decks,a multi-scale finite element model was set up to analyze the stress distribution characteristics and the load test was conducted on the Taizhou Yangtze River Bridge.Comparing the vehicle test results with the muli-scale finite element model results to verify the accuracy of the finite element simulation for the stress response of two welded details.The results indicated that The stress at the rib-to-deck welded connection and the rib-to-rib welded connection are the bending stress and the membrane stress,respectively;the stress response of the two welded connection has strong local characteristics;the lateral stress influence line of the two welded connection is relatively short and the length of the lateral stress influence line is greatly affected by the longitudinal ribs;increasing the thickness of the roof and longitudinal ribs can reduce the stress response and improve the stress performance of the heavy lanes.For the two welded details,the fatigue damage increment of the ordinary lane is greater than the heavy lane.The thickened roof and longitudinal ribs at the position of the heavy lane still cannot balance the fatigue damage caused by the heavy truck.Therefore,it is necessary to strictly control the fatigue effect of overloaded vehicles on steel box girders.
基金Supported by National Natural Science Foundation of China(Grant No.51805447)Natural Science Foundation of Jiangsu Higher Education of China(Grant No.22KJB460010)+2 种基金Jiangsu Provincial Innovation and Promotion Project of Forestry Science and Technology of China(Grant No.LYKJ[2023]06)Yangzhou Science and Technology Plan(City School Cooperation Project)of China(Grant No.YZ2022193)Cyan Blue Project of Yangzhou University of China。
文摘In the realm of engineering practice,various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra.Thus,accurately predicting the fatigue life of structures becomes notably arduous.This paper proposed an approach to predict the fatigue life of structure based on the optimized load spectra,which is accurately estimated by an efficient hinging hyperplane neural network(EHH-NN)model.The construction of the EHH-NN model includes initial network generation and parameter optimization.Through the combination of working conditions design,multi-body dynamics analysis and structural static mechanics analysis,the simulated load spectra of the structure are obtained.The simulated load spectra are taken as the input variables for the optimized EHH-NN model,while the measurement load spectra are used as the output variables.The prediction results of case structure indicate that the optimized EHH-NN model can achieve the high-accuracy load spectra,in comparison with support vector machine(SVM),random forest(RF)model and back propagation(BP)neural network.The error rate between the prediction values and the measurement values of the optimized EHH-NN model is 4.61%.In the Cauchy-Lorentz distribution,the absolute error data of 92%with EHH-NN model appear in the intermediate range of±1.65%.Also,the fatigue life analysis is performed for the case structure,based on the accurately predicted load spectra.The fatigue life of the case structure is calculated based on the comparison between the measured and predicted load spectra,with an accuracy of 93.56%.This research proposes the optimized EHH-NN model can more accurately reflect the measurement load spectra,enabling precise calculation of fatigue life.Additionally,the optimized EHH-NN model provides reliability assessment for industrial engineering equipment.
基金supported in part by the Fundamental Research Funds for the Liaoning Universities(LJ212410146025)the Graduate Science and Technology Innovation Project of University of Science and Technology Liaoning(LKDYC202310).
文摘To fully explore the potential features contained in power load data,an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed.Firstly,a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data,accurately dividing subsets of data into different categories.Secondly,introducing convolutional block attention mechanism into the bidirectional gated recurrent unit(BiGRU)structure significantly enhances its ability to extract key features.On this basis,in order to make the model more accurately adapt to the dynamic changes in power load data,subsets of different categories of data were used for BiGRU training based on attention mechanism,and extreme gradient boosting was selected as the meta model to effectively integrate multiple sets of historical training information.To further optimize the parameter configuration of the meta model,Bayesian optimization techniques are used to achieve automated adjustment of hyperparameters.Multiple sets of comparative experiments were designed,and the results showed that the average absolute error of the method in this paper was reduced by about 8.33%and 4.28%,respectively,compared with the single model and the combined model,and the determination coefficient reached the highest of 95.99,which proved that the proposed method has a better prediction effect.
基金supported by JUST Start-up Fund for Science Research,the Jiangsu Natural Science Foundation(Grant No.BK20210885).
文摘The internal solitary wave(ISW)represents a frequent and severe oceanic dynamic phenomenon observed in the South China Sea,exposing marine structures to sudden loads.This paper examines the prediction model of interaction loads between ISW and FPSO,accounting for varying attack angles and incorporating ISW theories.The research demonstrates that the horizontal and transverse forces on FPSO under internal solitary waves(ISWs)comprise wave pressure difference force and viscous force,while the vertical force primarily consists of vertical wave pressure difference force.The wave pressure difference force is determined using the Froude-Krylov equation.The viscous force is derived from the tangential particle velocity induced by ISW and the viscous coefficient.The viscous coefficient formula is obtained through regression analysis of experimental data with different ISW attack angles.The research reveals that the horizontal viscous coefficient C_(vx)decreases as Reynolds number(R_(e))increases,while the transverse viscous coefficient C_(vy)initially increases and subsequently decreases with the growth of the Keulegan-Carpenter number(KC).Moreover,changes in wave propagation direction significantly affect the extreme magnitudes of both horizontal and transverse forces,and simultaneously modify the transverse force orientation,while having minimal impact on the vertical force.Additionally,the forces increase with the ISW’s amplitude.For horizontal and transverse forces,a thinner upper fluid layer generates larger forces.Comparative analysis of experimental,numerical,and theoretical results indicates strong agreement between theoretical predictions and experimental and numerical outcomes.
文摘针对非侵入式负荷分解方法负荷特征捕捉不足、负荷分解精度不够等问题,文章提出一种基于改进BERT(bidirectional encoder representations from transformers)模型的多头自注意力非侵入式负荷分解方法(frequency and temporal attention-BERT, FAT-BERT)。首先通过傅里叶变换将时域数据转换为频域数据,采用多尺度卷积全面捕捉负荷信号的时域和频域特征,从而增强模型对多样化负荷信号的表达能力;其次,在多头自注意力机制中引入频率注意力机制,从而增强模型对时序数据中频率成分的感知能力,进一步改善复杂负荷模式的表示,改进BERT模型中增加局部自注意力从而减少不必要的全局计算,提升模型的运行速度;接着将残差连接和正则化技术结合使模型在训练过程中更加稳定,并且能够更好地避免过拟合,最后在REDD和UK-DALE数据集上对提出的方法进行实验,实验结果验证了所提方法的有效性。
文摘负荷预测是综合能源系统(integrated energy system,IES)高效运行的前提,面对综合能源系统多元负荷强耦合相关性、强随机性的特点,单一模型在运行负荷特征提取方面存在不足。为充分利用负荷间的相关性、降低负荷数据的非平稳性、弥补单一模型的不足,提出一种基于TCN-TPABiLSTM组合模型和多任务学习框架的IES多元负荷超短期协同预测方法。首先对负荷间耦合相关性、负荷时间相关性和负荷影响因素进行分析以构建模型输入,再通过变分模态分解将负荷数据分解为一定数量的模态以降低非平稳性,最后以TCN-TPA-BiLSTM组合模型作为多任务学习框架的共享层进行预测。通过实际数据进行验证和对比,结果表明该方法能够充分发挥模型各部分优势,相较于其他模型也获得了更优的结果。
文摘在信息化蓬勃发展的今日,大量云计算资源的高效管理是运维领域的重要难题。准确的负载预测是应对这一难题的关键技术。针对该问题提出一种基于局部加权回归周期趋势分解算法(Seasonal and Trend decomposition using Loess,STL)、Holt-Winters模型和深度自回归模型(DeepAR)的组合预测模型STL-DeepAR-HW。先采用快速傅里叶变换和自相关函数提取数据的周期性特征,以提取到的最优周期对数据做STL分解,将数据分解为趋势项、季节项和余项;并用DeepAR和Holt-Winters分别预测趋势项和季节项,最后组合得到预测结果。在公开数据集AzurePublicDataset上进行实验,结果表明,与Transformer、Stacked-LSTM以及Prophet等模型相比,该组合模型在负载预测中具有更高的准确性和适用性。
基金supported by National Basic Research Program of China (Grant Nos. 2010CB950300, 2010CB950500)Public Science and Technology Research Funds Projects of Ocean (Grant No. 201105019)+1 种基金Key Supercomputing Science-Technology Project of Shandong Province of China (Grant No. 2011YD01107)Scientific Research Foundation of the First Institute of Oceanography, State Oceanic Administration (Grant No. GY02-2010G22)
文摘To achieve high parallel efficiency for the global MASNUM surface wave model, the algorithm of an irregular quasirectangular domain decomposition and related serializing of calculating points and data exchanging schemes are developed and conducted, based on the environment of Message Passing Interface(MPI). The new parallel version of the surface wave model is tested for parallel computing on the platform of the Sunway BlueLight supercomputer in the National Supercomputing Center in Jinan. The testing involves four horizontal resolutions, which are 1°×1°,(1/2)°×(1/2)°,(1/4)°×(1/4)°, and(1/8)°×(1/8)°. These tests are performed without data Input/Output(IO) and the maximum amount of processors used in these tests reaches to 131072. The testing results show that the computing speeds of the model with different resolutions are all increased with the increasing of numbers of processors. When the number of processors is four times that of the base processor number, the parallel efficiencies of all resolutions are greater than 80%. When the number of processors is eight times that of the base processor number, the parallel efficiency of tests with resolutions of 1°×1°,(1/2)°×(1/2)° and(1/4)°×(1/4)° is greater than 80%, and it is 62% for the test with a resolution of(1/8)°×(1/8)° using 131072 processors, which is the nearly all processors of Sunway BlueLight. When the processor's number is 24 times that of the base processor number, the parallel efficiencies for tests with resolutions of 1°×1°,(1/2)°×(1/2)°, and(1/4)°×(1/4)° are 72%, 62%, and 38%, respectively. The speedup and parallel efficiency indicate that the irregular quasi-rectangular domain decomposition and serialization schemes lead to high parallel efficiency and good scalability for a global numerical wave model.
