In order to improve the condition monitoring and fault diagnosis of wind turbines,a stacked noise reduction autoencoding network based on group normalization is proposed in this paper.The network is based on SCADA dat...In order to improve the condition monitoring and fault diagnosis of wind turbines,a stacked noise reduction autoencoding network based on group normalization is proposed in this paper.The network is based on SCADA data of wind turbine operation,firstly,the group normalization(GN)algorithm is added to solve the problems of stack noise reduction autoencoding network training and slow convergence speed,and the RMSProp algorithm is used to update the weight and the bias of the autoenccoder,which further optimizes the problem that the loss function swings too much during the update process.Finally,in the last layer of the network,the softmax activation function is used to classify the results,and the output of the network is transformed into a probability distribution.The selected wind turbine SCADA data was substituted into the pre-improved and improved stacked denoising autoencoding(SDA)networks for comparative training and verification.The results show that the stacked denoising autoencoding network based on group normalization is more accurate and effective for wind turbine condition monitoring and fault diagnosis,and also provides a reference for wind turbine fault identification.展开更多
受地层调谐效应和地震数据品质等因素的影响,角度域共成像点道集(angle domain common image gathers,ADCIGs)存在不同程度的波形拉伸和随机噪声干扰。为了提高ADCIGs及其叠加剖面的成像效果,提出了基于奇异值分解的角度域去噪方法。首...受地层调谐效应和地震数据品质等因素的影响,角度域共成像点道集(angle domain common image gathers,ADCIGs)存在不同程度的波形拉伸和随机噪声干扰。为了提高ADCIGs及其叠加剖面的成像效果,提出了基于奇异值分解的角度域去噪方法。首先对叠前偏移输出的ADCIGs进行奇异值分解,然后对奇异值进行归一化修正,采用累计贡献率的方法确定降噪阶次,从而实现角度域内的信噪分离和噪声压制。在确定降噪阶次时,采用累计贡献率的方法可以直观地判断各奇异值分量对数据的贡献,便于快速选择降噪阶次。理论模型和实际数据的测试处理结果表明,基于奇异值分解的角度域去噪方法适用于具有水平同相轴的ADCIGs,它能有效分离角度域内的随机干扰,并且能压制高角度处的频率畸变,改善大角度数据的品质。对ADCIGs进行基于奇异值分解的角度域去噪,可进一步提高该叠前道集的精度,从而有效改善角度域叠加剖面的信噪比和分辨率,也为基于叠前道集的速度分析和叠前反演提供了更为准确的数据基础。展开更多
首先,阐述了需求响应资源的概率分布特性,并以IES运行成本为优化目标,综合考虑电力系统、天然气系统运行约束及能量耦合约束。建立IES最优能量流(optimal energy flow,OEF)模型,用于求取发电机和耦合环节功率,并将其作为电力系统稳定器...首先,阐述了需求响应资源的概率分布特性,并以IES运行成本为优化目标,综合考虑电力系统、天然气系统运行约束及能量耦合约束。建立IES最优能量流(optimal energy flow,OEF)模型,用于求取发电机和耦合环节功率,并将其作为电力系统稳定器的输入;其次,通过搭建不同负荷水平下的暂态仿真模型,得到故障情况下的系统稳定情况;然后,提出基于堆栈降噪自动编码器(stacked denoising auto-encoders,SDAE)的电力系统稳定性评估器的训练方法;最后,在IEEE.39节点电力系统和修改的比利时20节点天然气系统组成的IES中,进行电力系统稳定性智能化评估的算例分析。仿真结果表明,基于SDAE的电力系统稳定性评估器识别精度较高,同时计算效率也较优。展开更多
基金the National Natural Science Foundation of China(51767014),2018–2021.
文摘In order to improve the condition monitoring and fault diagnosis of wind turbines,a stacked noise reduction autoencoding network based on group normalization is proposed in this paper.The network is based on SCADA data of wind turbine operation,firstly,the group normalization(GN)algorithm is added to solve the problems of stack noise reduction autoencoding network training and slow convergence speed,and the RMSProp algorithm is used to update the weight and the bias of the autoenccoder,which further optimizes the problem that the loss function swings too much during the update process.Finally,in the last layer of the network,the softmax activation function is used to classify the results,and the output of the network is transformed into a probability distribution.The selected wind turbine SCADA data was substituted into the pre-improved and improved stacked denoising autoencoding(SDA)networks for comparative training and verification.The results show that the stacked denoising autoencoding network based on group normalization is more accurate and effective for wind turbine condition monitoring and fault diagnosis,and also provides a reference for wind turbine fault identification.
文摘受地层调谐效应和地震数据品质等因素的影响,角度域共成像点道集(angle domain common image gathers,ADCIGs)存在不同程度的波形拉伸和随机噪声干扰。为了提高ADCIGs及其叠加剖面的成像效果,提出了基于奇异值分解的角度域去噪方法。首先对叠前偏移输出的ADCIGs进行奇异值分解,然后对奇异值进行归一化修正,采用累计贡献率的方法确定降噪阶次,从而实现角度域内的信噪分离和噪声压制。在确定降噪阶次时,采用累计贡献率的方法可以直观地判断各奇异值分量对数据的贡献,便于快速选择降噪阶次。理论模型和实际数据的测试处理结果表明,基于奇异值分解的角度域去噪方法适用于具有水平同相轴的ADCIGs,它能有效分离角度域内的随机干扰,并且能压制高角度处的频率畸变,改善大角度数据的品质。对ADCIGs进行基于奇异值分解的角度域去噪,可进一步提高该叠前道集的精度,从而有效改善角度域叠加剖面的信噪比和分辨率,也为基于叠前道集的速度分析和叠前反演提供了更为准确的数据基础。
文摘首先,阐述了需求响应资源的概率分布特性,并以IES运行成本为优化目标,综合考虑电力系统、天然气系统运行约束及能量耦合约束。建立IES最优能量流(optimal energy flow,OEF)模型,用于求取发电机和耦合环节功率,并将其作为电力系统稳定器的输入;其次,通过搭建不同负荷水平下的暂态仿真模型,得到故障情况下的系统稳定情况;然后,提出基于堆栈降噪自动编码器(stacked denoising auto-encoders,SDAE)的电力系统稳定性评估器的训练方法;最后,在IEEE.39节点电力系统和修改的比利时20节点天然气系统组成的IES中,进行电力系统稳定性智能化评估的算例分析。仿真结果表明,基于SDAE的电力系统稳定性评估器识别精度较高,同时计算效率也较优。
