由于可再生能源和负荷的不确定性,电力系统潮流分析需要有效的工具。目前的多数研究都假设一组给定的概率密度函数(PDF:Probability Density Functions)建模不确定性,并开发参数概率潮流工具。为此,提出了一种非参数概率潮流分析方法确...由于可再生能源和负荷的不确定性,电力系统潮流分析需要有效的工具。目前的多数研究都假设一组给定的概率密度函数(PDF:Probability Density Functions)建模不确定性,并开发参数概率潮流工具。为此,提出了一种非参数概率潮流分析方法确定潮流输出的偏微分方程。该方法基于平均值一阶鞍点近似。对于n个随机变量系统,利用潮流计算建立一阶Taylor级数展开,然后采用鞍点近似确定期望输出变量的概率特性。所提出的非参数估计器在需要合理的计算量的同时,能提供精确的结果。此外,在不使用积分或微分算子的情况下,直接建立了潮流输出的概率分布函数和累积分布函数。在IEEE 14总线和IEEE 118总线测试系统上进行了测试,所得结果与其他方法相比,MVFOSPA(Mean Value First Order Saddle Point Approximation)比MCS(Monte Carlo Simulation)算法运行时间减少了12%,验证了MVFOSPA方法的有效性。展开更多
To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-le...To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed.First,according to the impact characteristics of rolling bearing faults,correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals.These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals.Subsequently,a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings.Finally,the fused features were fed into a Softmax classifier to complete the fault diagnosis.The results show that the proposed method exhibits an average test accuracy of over 99.00%on the two rolling bearing fault datasets,outperforming other comparison methods.Thus,the method can be effectively utilized for diagnosing rolling bearing faults.展开更多
文摘由于可再生能源和负荷的不确定性,电力系统潮流分析需要有效的工具。目前的多数研究都假设一组给定的概率密度函数(PDF:Probability Density Functions)建模不确定性,并开发参数概率潮流工具。为此,提出了一种非参数概率潮流分析方法确定潮流输出的偏微分方程。该方法基于平均值一阶鞍点近似。对于n个随机变量系统,利用潮流计算建立一阶Taylor级数展开,然后采用鞍点近似确定期望输出变量的概率特性。所提出的非参数估计器在需要合理的计算量的同时,能提供精确的结果。此外,在不使用积分或微分算子的情况下,直接建立了潮流输出的概率分布函数和累积分布函数。在IEEE 14总线和IEEE 118总线测试系统上进行了测试,所得结果与其他方法相比,MVFOSPA(Mean Value First Order Saddle Point Approximation)比MCS(Monte Carlo Simulation)算法运行时间减少了12%,验证了MVFOSPA方法的有效性。
基金The National Natural Science Foundation of China(No.U22A20178)National Key Research and Development Program of China(No.2022YFB3404800)Jiangsu Province Science and Technology Achievement Transformation Special Fund Program(No.BA2023019).
文摘To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed.First,according to the impact characteristics of rolling bearing faults,correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals.These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals.Subsequently,a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings.Finally,the fused features were fed into a Softmax classifier to complete the fault diagnosis.The results show that the proposed method exhibits an average test accuracy of over 99.00%on the two rolling bearing fault datasets,outperforming other comparison methods.Thus,the method can be effectively utilized for diagnosing rolling bearing faults.
