Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
水泥生产立磨出风口温度是判断立磨运行状态是否安全稳定的关键参数,对该参数提前预测可以减少立磨振动,提高运行稳定性,增加产量,降低能耗及相关碳排放。水泥立磨系统具有多参数、大时滞和非线性等复杂特性。针对上述问题,提出了基于...水泥生产立磨出风口温度是判断立磨运行状态是否安全稳定的关键参数,对该参数提前预测可以减少立磨振动,提高运行稳定性,增加产量,降低能耗及相关碳排放。水泥立磨系统具有多参数、大时滞和非线性等复杂特性。针对上述问题,提出了基于互相关延时分析优化的非线性自回归外部输入(Nonlinear AutoRegressive with eXogenous inputs,NARX)神经网络,并用于立磨出风口温度预测。首先,采用皮尔逊相关性分析从多个参数中确定影响立磨出风口温度的关键参数。同时,利用互相关延时分析进行时滞分析,解决大时滞问题。其次,通过优化的NARX神经网络,实现非线性工况下温度的精准预测。案例验证结果表明,所提出模型的拟合度达到了0.99967,均方误差为0.56483,预测精度达到了98.4%以上。预测模型结果可指导立磨操作人员及时控制立磨振动,提高水泥产量并降低能耗和碳排放。展开更多
高比例新能源接入使配电网具备一定的主动电压支撑能力,可通过调节公共连接点无功功率,实现输配协同电压调控。然而,新能源出力波动及输配耦合效应加剧了电压失稳过程的复杂性,给短期电压稳定(short-term voltage stability,STVS)评估...高比例新能源接入使配电网具备一定的主动电压支撑能力,可通过调节公共连接点无功功率,实现输配协同电压调控。然而,新能源出力波动及输配耦合效应加剧了电压失稳过程的复杂性,给短期电压稳定(short-term voltage stability,STVS)评估带来挑战。为此,提出计及输配协同的STVS数据驱动评估方法,首先,区别于传统评估中将配电网简化为不可控等值负荷,构建计及配电网主动电压支撑能力的系统时域仿真拓展模型,基于优化方法量化支撑能力并嵌入时域仿真,反映其对电压稳定的影响。其次,基于该模型与历史数据,构建以系统量测量为输入、稳定性状态为输出的训练数据集,训练卷积神经网络(convolutional neural network,CNN)实现STVS在线评估。相比于现有基于深度学习的STVS评估,提出了基于关键节点电压的输入-输出变量降维提取方法,可显著减少训练数据量,提升学习效率。算例仿真结果验证了所提方法在STVS评估和电压失稳程度量化方面的有效性。展开更多
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
文摘水泥生产立磨出风口温度是判断立磨运行状态是否安全稳定的关键参数,对该参数提前预测可以减少立磨振动,提高运行稳定性,增加产量,降低能耗及相关碳排放。水泥立磨系统具有多参数、大时滞和非线性等复杂特性。针对上述问题,提出了基于互相关延时分析优化的非线性自回归外部输入(Nonlinear AutoRegressive with eXogenous inputs,NARX)神经网络,并用于立磨出风口温度预测。首先,采用皮尔逊相关性分析从多个参数中确定影响立磨出风口温度的关键参数。同时,利用互相关延时分析进行时滞分析,解决大时滞问题。其次,通过优化的NARX神经网络,实现非线性工况下温度的精准预测。案例验证结果表明,所提出模型的拟合度达到了0.99967,均方误差为0.56483,预测精度达到了98.4%以上。预测模型结果可指导立磨操作人员及时控制立磨振动,提高水泥产量并降低能耗和碳排放。
文摘高比例新能源接入使配电网具备一定的主动电压支撑能力,可通过调节公共连接点无功功率,实现输配协同电压调控。然而,新能源出力波动及输配耦合效应加剧了电压失稳过程的复杂性,给短期电压稳定(short-term voltage stability,STVS)评估带来挑战。为此,提出计及输配协同的STVS数据驱动评估方法,首先,区别于传统评估中将配电网简化为不可控等值负荷,构建计及配电网主动电压支撑能力的系统时域仿真拓展模型,基于优化方法量化支撑能力并嵌入时域仿真,反映其对电压稳定的影响。其次,基于该模型与历史数据,构建以系统量测量为输入、稳定性状态为输出的训练数据集,训练卷积神经网络(convolutional neural network,CNN)实现STVS在线评估。相比于现有基于深度学习的STVS评估,提出了基于关键节点电压的输入-输出变量降维提取方法,可显著减少训练数据量,提升学习效率。算例仿真结果验证了所提方法在STVS评估和电压失稳程度量化方面的有效性。
