A Compressive Sensing Based Method for Harmonic State Estimation

Power quality monitoring has become a vital need in modern power systems owing to the need for agile operation and troubleshooting scheme. On the other hand, the nature of load in modern power system is changing in many ways. Digital loads, which are mostly relied on power electronic equipment, may distort the quality of power flowing through the network. Moreover, one of the most critical objectives of smart grids is to improve quality of services delivered to customers, alongside with security, reliability and efficiency. To this end, a novel method based on compressive sensing is proposed in this paper to detect the source and the magnitude of the harmonics. The method takes advantages of compressive sensing theory in such a way that a real-time monitoring of harmonic distortion is obtained with a limited number of measurements. The efficacy of the method is checked by means of various simulations on IEEE 118 bus test system. The results show the capabilities of the method in both noisy and noise-free conditions

Dimensionality reduction for visualization of normal and pathological speech data

For an adequate analysis of pathological speech signals, a sizeable number of parameters is required, such as those related to jitter, shimmer and noise content. Often this kind of high-dimensional signal representation is difficult to understand, even for expert voice therapists and physicians. Data visualization of a high-dimensional dataset can provide a useful first step in its exploratory data analysis, facilitating an understanding about its underlying structure. In the present paper, eight dimensionality reduction techniques, both classical and recent, are compared on speech data containing normal and pathological speech. A qualitative analysis of their dimensionality reduction capabilities is presented. The transformed data are also quantitatively evaluated, using classifiers, and it is found that it may be advantageous to perform the classification process on the transformed data, rather than on the original. These qualitative and quantitative analyses allow us to conclude that a nonlinear, supervised method, called kernel local Fisher discriminant analysis is superior for dimensionality reduction in the actual context.Fil: Goddard, J.. Universidad Autónoma Metropolitana; MéxicoFil: Schlotthauer, Gaston. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Torres, Maria Eugenia. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería. Departamento de Matemática e Informática. Laboratorio de Señales y Dinámicas no Lineales; ArgentinaFil: Rufiner, Hugo Leonardo. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin