The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identific...The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.展开更多
时频分析是气藏烃类检测的常用手段,常规时频分析手段存在时频谱模糊化的问题,难以满足高精度分析的要求。同步挤压S变换(Synchrosqueezing S Transform,SSST)通过对时频能量的重新挤压,能获得更高的时频分辨率。针对同步挤压S变换处理...时频分析是气藏烃类检测的常用手段,常规时频分析手段存在时频谱模糊化的问题,难以满足高精度分析的要求。同步挤压S变换(Synchrosqueezing S Transform,SSST)通过对时频能量的重新挤压,能获得更高的时频分辨率。针对同步挤压S变换处理实际三维地震资料时计算量大的问题,提出采用OpenMP并行计算技术提高计算效率,测试表明加速效果显著。结合大牛地气田的实际地质背景,建立了典型的煤层气地质模型,用SSST分析了30 Hz地震子波下该地质模型的非稳态地震正演响应的时频特征,结果表明,可基于时频特征预测气田8#煤层的含气性。在此基础上,使用SSST对大牛地气田某小区的8#煤层含气富集区进行了预测,指导了某风险探井的部署,该井煤层钻遇率82.9%,实钻井的气测全烃钻遇情况和生产情况证实了SSST在煤层含气富集区地震信号频变信息检测的有效性,为非常规气藏开发提供了技术支撑,应用潜力大。展开更多
地震波穿过油气储层时会发生高频能量的异常衰减,通过计算频率衰减梯度能够反应地下储层的分布情况.为了有效提高计算频率衰减梯度的精度,提出一种基于局部最大同步挤压变换(Local Maximum Synchrosqueezing Transform,LMSST)的频率衰...地震波穿过油气储层时会发生高频能量的异常衰减,通过计算频率衰减梯度能够反应地下储层的分布情况.为了有效提高计算频率衰减梯度的精度,提出一种基于局部最大同步挤压变换(Local Maximum Synchrosqueezing Transform,LMSST)的频率衰减梯度计算方法.该方法首先利用LMSST时频变换对地震信号进行时频分析,然后采用基于最小二乘法的Nelder-Mead拟合方法对高频段进行拟合,获得衰减梯度值.理论调频-调幅信号、单道合成地震信号和单道实测地震信号表明LMSST具有更高时频聚集性和抗噪能力,进而能够提升地震信号的时频分辨率.在实际的地震资料应用中,本文提出的方法相比STFT、FSST、FSST2和FSST4具有更高的计算精度,与实际油气测试井更吻合,为计算地震信号的频率衰减梯度提供了一种新技术.展开更多
为提高非平稳响应信号瞬时频率的识别效果,提出基于滑动窗宽优化的局部最大同步挤压广义S变换(local maximum synchrosqueezing generalized S-transform,LMSSGST)。该方法首先对非平稳响应信号进行广义S变换获得相应的时频系数;其次,...为提高非平稳响应信号瞬时频率的识别效果,提出基于滑动窗宽优化的局部最大同步挤压广义S变换(local maximum synchrosqueezing generalized S-transform,LMSSGST)。该方法首先对非平稳响应信号进行广义S变换获得相应的时频系数;其次,利用该响应信号的功率谱密度特征曲线确定局部最大同步挤压算子中滑动窗的宽度;再次,通过局部最大同步挤压算子进行时频重排;最后,采用模极大值改进算法提取瞬时频率曲线。通过两个数值算例、一个滑动窗宽参数分析和一个时变拉索试验验证了所提方法的有效性,研究结果表明:利用功率谱密度特征曲线能够有效确定滑动窗的窗宽和模极大值算法的提取范围。相比局部最大同步挤压变换算法,基于滑动窗宽优化的LMSSGST具有更佳的瞬时频率识别效果。展开更多
The analysis of accuracy for superposition of squeezed states （SSSs） in lossless and loss case has been performed in this study. In lossless case, time accuracies of SSSs with mean photon number ns have a scaling of...The analysis of accuracy for superposition of squeezed states （SSSs） in lossless and loss case has been performed in this study. In lossless case, time accuracies of SSSs with mean photon number ns have a scaling of ns-2 in two limits of large and small squeezing. With the help of photon loss model, the dissipative channel will degrade accuracies has been proved. In the limit of large squeezing, the accuracy will slowly decrease with the reduction of transmittance η. In the limit of small squeezing, time accuracy scales as 1/（η4n2） and will decrease much faster along with η decreases.展开更多
基金supported by the Science and Technology Project of State Grid Corporation of China(5100202199536A-0-5-ZN)。
文摘The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation sourcelocalization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.
文摘时频分析是气藏烃类检测的常用手段,常规时频分析手段存在时频谱模糊化的问题,难以满足高精度分析的要求。同步挤压S变换(Synchrosqueezing S Transform,SSST)通过对时频能量的重新挤压,能获得更高的时频分辨率。针对同步挤压S变换处理实际三维地震资料时计算量大的问题,提出采用OpenMP并行计算技术提高计算效率,测试表明加速效果显著。结合大牛地气田的实际地质背景,建立了典型的煤层气地质模型,用SSST分析了30 Hz地震子波下该地质模型的非稳态地震正演响应的时频特征,结果表明,可基于时频特征预测气田8#煤层的含气性。在此基础上,使用SSST对大牛地气田某小区的8#煤层含气富集区进行了预测,指导了某风险探井的部署,该井煤层钻遇率82.9%,实钻井的气测全烃钻遇情况和生产情况证实了SSST在煤层含气富集区地震信号频变信息检测的有效性,为非常规气藏开发提供了技术支撑,应用潜力大。
文摘地震波穿过油气储层时会发生高频能量的异常衰减,通过计算频率衰减梯度能够反应地下储层的分布情况.为了有效提高计算频率衰减梯度的精度,提出一种基于局部最大同步挤压变换(Local Maximum Synchrosqueezing Transform,LMSST)的频率衰减梯度计算方法.该方法首先利用LMSST时频变换对地震信号进行时频分析,然后采用基于最小二乘法的Nelder-Mead拟合方法对高频段进行拟合,获得衰减梯度值.理论调频-调幅信号、单道合成地震信号和单道实测地震信号表明LMSST具有更高时频聚集性和抗噪能力,进而能够提升地震信号的时频分辨率.在实际的地震资料应用中,本文提出的方法相比STFT、FSST、FSST2和FSST4具有更高的计算精度,与实际油气测试井更吻合,为计算地震信号的频率衰减梯度提供了一种新技术.
文摘为提高非平稳响应信号瞬时频率的识别效果,提出基于滑动窗宽优化的局部最大同步挤压广义S变换(local maximum synchrosqueezing generalized S-transform,LMSSGST)。该方法首先对非平稳响应信号进行广义S变换获得相应的时频系数;其次,利用该响应信号的功率谱密度特征曲线确定局部最大同步挤压算子中滑动窗的宽度;再次,通过局部最大同步挤压算子进行时频重排;最后,采用模极大值改进算法提取瞬时频率曲线。通过两个数值算例、一个滑动窗宽参数分析和一个时变拉索试验验证了所提方法的有效性,研究结果表明:利用功率谱密度特征曲线能够有效确定滑动窗的窗宽和模极大值算法的提取范围。相比局部最大同步挤压变换算法,基于滑动窗宽优化的LMSSGST具有更佳的瞬时频率识别效果。
基金supported by the National Natural Science Foundation of China (Grant No. 61075014)the Science Foundation of Xi’an University of Posts and Telecommunications for Young Teachers (Grant No.ZL2010-11)the Science Foundation of Shaanxi Provincial Department of Education (Grant No. 11JK0902)
文摘The analysis of accuracy for superposition of squeezed states （SSSs） in lossless and loss case has been performed in this study. In lossless case, time accuracies of SSSs with mean photon number ns have a scaling of ns-2 in two limits of large and small squeezing. With the help of photon loss model, the dissipative channel will degrade accuracies has been proved. In the limit of large squeezing, the accuracy will slowly decrease with the reduction of transmittance η. In the limit of small squeezing, time accuracy scales as 1/（η4n2） and will decrease much faster along with η decreases.
