A novel phase-shifted long-period fiber grating(PS-LPFG)for the simultaneous measurement of torsion and temperature is described and experimentally demonstrated.The PS-LPFG is fabricated by inserting a pretwisted stru...A novel phase-shifted long-period fiber grating(PS-LPFG)for the simultaneous measurement of torsion and temperature is described and experimentally demonstrated.The PS-LPFG is fabricated by inserting a pretwisted structure into the long-period fiber grating(LPFG)written in single-mode fiber(SMF).Experimental results show that the torsion sensitivities of the two dips are?0.114 nm/(rad/m)and?0.069 nm/(rad/m)in the clockwise direction,and?0.087 nm/(rad/m)and?0.048 nm/(rad/m)in the counterclockwise direction,respectively.The temperature sensitivities of the two dips are 0.057 nm/℃ and 0.051 nm/℃,respectively.The two dips of the PS-LPFG exhibit different responses to torsion and temperature.Simultaneous measurement of torsion and temperature can be implemented using a sensor.The feasibility and stabilization of simultaneous torsion and temperature measurement have been confirmed,and hence this novel PS-LPFG demonstrates potential for fiber sensing and engineering applications.展开更多
提出一种基于广义回归神经网络(generalized regression neural network,GRNN)的风力发电机组性能预测及异常状态预警方法。通过分析运行中影响风机主轴转速和发电功率的主要因素,确定了性能预测模型的输入和输出参数。运用监控与数据采...提出一种基于广义回归神经网络(generalized regression neural network,GRNN)的风力发电机组性能预测及异常状态预警方法。通过分析运行中影响风机主轴转速和发电功率的主要因素,确定了性能预测模型的输入和输出参数。运用监控与数据采集(supervisory control and data acquisition,SCADA)系统的真实历史数据,采用广义回归神经网络(GRNN)建立了风电机组的性能预测模型,通过比较模型的预测精度对GRNN的平滑因子进行了优选。以此模型为基础,采用滑动数据窗方法实时计算风电机组转速和功率的残差评价指标,当评价指标连续超过预先设定的阈值时,则可判断风电机组状态异常。采用某实际风电机组若干历史故障发生前后的真实SCADA数据进行模拟,验证了方法的有效性。展开更多
为改善微网中储能逆变器的输出电压波形质量,提出了一种基于神经网络的逆模型控制方法。建立了储能逆变器的数学模型,分析了影响其输出电压的主要因素,利用前向神经网络建立了系统的扩展逆模型;针对BP训练算法容易陷入局部最优的问题,...为改善微网中储能逆变器的输出电压波形质量,提出了一种基于神经网络的逆模型控制方法。建立了储能逆变器的数学模型,分析了影响其输出电压的主要因素,利用前向神经网络建立了系统的扩展逆模型;针对BP训练算法容易陷入局部最优的问题,通过万有引力算法进行了网络初始参数优化;将神经网络逆模型与原模型串联后,采用PI控制器实施闭环控制。仿真结果表明,该方法可以有效的提高储能逆变器的动态响应速度,并降低输出电压的谐波含量。制作了10 k W储能逆变器样机进行试验,结果表明了所提方法的可行性与有效性。展开更多
基金supported by the National Natural Science Foundation of China(No.11527804)Joint Research Fund in Astronomy under cooperative agreement between the National Natural Science Foundation of China(NSFC)and Chinese Academy of Sciences(CAS)(Nos.U1831115 and U1631239)+3 种基金Open Project of Key Laboratory of Astronomical Optics&Technology,Nanjing Institute of Astronomical Optics&Technology,Chinese Academy of Sciences(No.CAS-KLAOT-KF201806)Fundamental Research Funds for the Central Universities111 Project(No.B13015)the Harbin Engineering University.
文摘A novel phase-shifted long-period fiber grating(PS-LPFG)for the simultaneous measurement of torsion and temperature is described and experimentally demonstrated.The PS-LPFG is fabricated by inserting a pretwisted structure into the long-period fiber grating(LPFG)written in single-mode fiber(SMF).Experimental results show that the torsion sensitivities of the two dips are?0.114 nm/(rad/m)and?0.069 nm/(rad/m)in the clockwise direction,and?0.087 nm/(rad/m)and?0.048 nm/(rad/m)in the counterclockwise direction,respectively.The temperature sensitivities of the two dips are 0.057 nm/℃ and 0.051 nm/℃,respectively.The two dips of the PS-LPFG exhibit different responses to torsion and temperature.Simultaneous measurement of torsion and temperature can be implemented using a sensor.The feasibility and stabilization of simultaneous torsion and temperature measurement have been confirmed,and hence this novel PS-LPFG demonstrates potential for fiber sensing and engineering applications.
文摘提出一种基于广义回归神经网络(generalized regression neural network,GRNN)的风力发电机组性能预测及异常状态预警方法。通过分析运行中影响风机主轴转速和发电功率的主要因素,确定了性能预测模型的输入和输出参数。运用监控与数据采集(supervisory control and data acquisition,SCADA)系统的真实历史数据,采用广义回归神经网络(GRNN)建立了风电机组的性能预测模型,通过比较模型的预测精度对GRNN的平滑因子进行了优选。以此模型为基础,采用滑动数据窗方法实时计算风电机组转速和功率的残差评价指标,当评价指标连续超过预先设定的阈值时,则可判断风电机组状态异常。采用某实际风电机组若干历史故障发生前后的真实SCADA数据进行模拟,验证了方法的有效性。
基金supported by the National Natural Science Foundation of China(Nos.61377084,41174161,and 61775044) the Joint Research Fund in Astronomy(No.U1631239)under cooperative agreement between the National Natural Science Foundation of China(NSFC)and the Chinese Academy of Sciences(CAS) in part by the Aeronautical Science Foundation of China(No.201608P6003)
文摘为改善微网中储能逆变器的输出电压波形质量,提出了一种基于神经网络的逆模型控制方法。建立了储能逆变器的数学模型,分析了影响其输出电压的主要因素,利用前向神经网络建立了系统的扩展逆模型;针对BP训练算法容易陷入局部最优的问题,通过万有引力算法进行了网络初始参数优化;将神经网络逆模型与原模型串联后,采用PI控制器实施闭环控制。仿真结果表明,该方法可以有效的提高储能逆变器的动态响应速度,并降低输出电压的谐波含量。制作了10 k W储能逆变器样机进行试验,结果表明了所提方法的可行性与有效性。