针对燃煤机组锅炉主再热汽温控制中存在的滞后性、多变量耦合及动态工况适应难题,文章提出一种融合数字孪生技术与最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的汽温寻优方法。通过构建锅炉三维数字孪生模型实现...针对燃煤机组锅炉主再热汽温控制中存在的滞后性、多变量耦合及动态工况适应难题,文章提出一种融合数字孪生技术与最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的汽温寻优方法。通过构建锅炉三维数字孪生模型实现设备状态实时映射,结合LS-SVM建立多变量动态预测模型,并引入多目标微分进化算法(MODE)进行参数优化。实际应用表明,该方法使主汽温波动范围从±7℃缩小至±2.5℃,再热汽温预测误差稳定在±1.5℃以内,年节约燃煤成本超400万元,为火电机组深度调峰与能效提升提供技术支撑。展开更多
In order to realize direct thrust control instead of conventional sensors-based control for aero-engine, a thrust estimator with high accuracy is designed by using the boosting technique to improve the performance of ...In order to realize direct thrust control instead of conventional sensors-based control for aero-engine, a thrust estimator with high accuracy is designed by using the boosting technique to improve the performance of least squares support vector regression （LSSVR）. There exist two distinct features compared with the conven- tional boosting technique： （1） Sampling without replacement is used to avoid numerical instability for modeling LSSVR. （2） To realize the sparseness of LSSVR and reduce the computational complexity, only a subset of the training samples is used to construct LSSVR. Thus, this boosting method for LSSVR is called the boosting sparse LSSVR （BSLSSVR）. Finally, simulation results show that BSLSSVR-based thrust estimator can satisfy the requirement of direct thrust control, i.e. , maximum absolute value of relative error of thrust estimation is not more than 5‰.展开更多
针对最大信息系数计算与传统支持向量机进行电力系统动态频率预测时计算或训练时间过长、精度较低、模型泛化性能较差等问题,通过改进最大信息系数计算的网格划分方法,并改造原有SVR模型的不等式约束条件以及模型损失函数,提出基于统计...针对最大信息系数计算与传统支持向量机进行电力系统动态频率预测时计算或训练时间过长、精度较低、模型泛化性能较差等问题,通过改进最大信息系数计算的网格划分方法,并改造原有SVR模型的不等式约束条件以及模型损失函数,提出基于统计信息系数与多输出最小二乘支持向量机(multi-output least squares support vector regreesion,MLSSVR)的频率响应曲线预测模型,实现对电力系统动态频率的特征提取、整体输出与链式预测,从而提升原有机器学习模型速度、精度和准确性。利用灰狼优化算法对MLSSVR算法中的核函数宽度以及惩罚因子进行寻优以提高模型综合性能。基于IEEE16机68节点的仿真实验证明,相较SVR算法及其他机器学习模型,本文所提模型的准确度、精度与训练预测速度均有提升,模型性能更强。展开更多
文摘针对燃煤机组锅炉主再热汽温控制中存在的滞后性、多变量耦合及动态工况适应难题,文章提出一种融合数字孪生技术与最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的汽温寻优方法。通过构建锅炉三维数字孪生模型实现设备状态实时映射,结合LS-SVM建立多变量动态预测模型,并引入多目标微分进化算法(MODE)进行参数优化。实际应用表明,该方法使主汽温波动范围从±7℃缩小至±2.5℃,再热汽温预测误差稳定在±1.5℃以内,年节约燃煤成本超400万元,为火电机组深度调峰与能效提升提供技术支撑。
基金Supported by the National Natural Science Foundation of China(50576033)the Aeronautical Science Foundation of China(04C52019)~~
文摘In order to realize direct thrust control instead of conventional sensors-based control for aero-engine, a thrust estimator with high accuracy is designed by using the boosting technique to improve the performance of least squares support vector regression （LSSVR）. There exist two distinct features compared with the conven- tional boosting technique： （1） Sampling without replacement is used to avoid numerical instability for modeling LSSVR. （2） To realize the sparseness of LSSVR and reduce the computational complexity, only a subset of the training samples is used to construct LSSVR. Thus, this boosting method for LSSVR is called the boosting sparse LSSVR （BSLSSVR）. Finally, simulation results show that BSLSSVR-based thrust estimator can satisfy the requirement of direct thrust control, i.e. , maximum absolute value of relative error of thrust estimation is not more than 5‰.
文摘针对最大信息系数计算与传统支持向量机进行电力系统动态频率预测时计算或训练时间过长、精度较低、模型泛化性能较差等问题,通过改进最大信息系数计算的网格划分方法,并改造原有SVR模型的不等式约束条件以及模型损失函数,提出基于统计信息系数与多输出最小二乘支持向量机(multi-output least squares support vector regreesion,MLSSVR)的频率响应曲线预测模型,实现对电力系统动态频率的特征提取、整体输出与链式预测,从而提升原有机器学习模型速度、精度和准确性。利用灰狼优化算法对MLSSVR算法中的核函数宽度以及惩罚因子进行寻优以提高模型综合性能。基于IEEE16机68节点的仿真实验证明,相较SVR算法及其他机器学习模型,本文所提模型的准确度、精度与训练预测速度均有提升,模型性能更强。
