GMPLS网络中的多元故障传播模型

现有流行病传播模型的应用范围仅限于一元故障或同构网络。为此,提出一个通用多协议标志交换(GMPLS)网络中的多元故障传播模型。将网络节点功能分为控制层和数据层,并给出故障传播阈值的计算公式。使用不同的拓扑结构进行模拟实验,结果均接近于理论值,验证了该模型的正确性。

基于多元暂态特征故障度的配电网单相接地选线方法

针对配电网发生单相接地故障时电流较弱、故障条件复杂、现有的故障检测技术性能不可靠的问题,提出一种利用基频移频的多元暂态特征故障度配电网单相接地选线方法。为了保留暂态特征的全景性,去除暂态零序电流中的基频分量,采用希尔伯特变换对暂态零序电流解析信号进行计算。之后,引入位移因子去除基频,保留所有的瞬态特征,并计算了3种典型瞬态特征指标。最后,采用Copula计算瞬态特征随机变量的联合分布密度函数并计算各线路的故障度,选择故障程度最大的馈线作为故障馈线。建立不同故障条件下的径向配电网样本模型、电弧故障模型以及风机和光伏模型的IEEE 34节点测试系统,验证了所提方法的有效性。

Performance of the geometric approach to fault detection and isolation in SISO,MISO,SIMO and MIMO systems

In this paper, a geometric approach to fault detection and isolation （FDI） is applied to a Multiple-Input Multipie-Output （MIMO） model of a frame and the FDI results are compared to the ones obtained in the Single-Input Single-Output （SISO）, Multiple-Input Single-Output （MISO）, and Single-Input Multiple-Output （SIMO） cases. A proper distance function based on parameters obtained from parametric system identification method is used in the geometric approach. ARX （Auto Regressive with exogenous input） and VARX （Vector ARX） models with 12 parameters are used in all of the above-mentioned models. The obtained results reveal that by increasing the number of inputs, the classification errors reduce, even in the case of applying only one of the inputs in the computations. Furthermore, increasing the number of measured outputs in the FDI scheme results in decreasing classification errors. Also, it is shown that by using probabilistic space in the distance function, fault diagnosis scheme has better performance in comparison with the deterministic one.

Application of Kernel Independent Component Analysis for Multivariate Statistical Process Monitoring

In this research, a new fault detection method based on kernel independent component analysis （kernel ICA） is developed. Kernel ICA is an improvement of independent component analysis （ICA）, and is different from kernel principal component analysis （KPCA） proposed for nonlinear process monitoring. The basic idea of our approach is to use the kernel ICA to extract independent components efficiently and to combine the selected essential independent components with process monitoring techniques. 12 （the sum of the squared independent scores） and squared prediction error （SPE） charts are adopted as statistical quantities. The proposed monitoring method is applied to Tennessee Eastman process, and the simulation results clearly show the advantages of kernel ICA monitoring in comparison to ICA monitoring.

Nonlinear industrial process fault diagnosis with latent label consistency and sparse Gaussian feature learning

With the increasing complexity of industrial processes, the high-dimensional industrial data exhibit a strong nonlinearity, bringing considerable challenges to the fault diagnosis of industrial processes. To efficiently extract deep meaningful features that are crucial for fault diagnosis, a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label dimension. The feature space is described by the one-hot encoding of the fault category label as an orthogonal basis. In this way, the deep sparse Gaussian features related to fault categories can be gradually learned from the raw data by SGFE. In the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian distributions. The sparse Gaussian features are linearly separable in the feature space, which is conducive to improving the accuracy of the downstream fault classification task. The feasibility and practical utility of the proposed SGFE are verified by the handwritten digits MNIST benchmark and Tennessee-Eastman(TE) benchmark process,respectively.