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A method is proposed for estimating interharmonic frequencies in power system voltage and current signals. The method is based on a spectrum-estimation method known as 'estimation of signal parameters via rotational invariance techniques' (ESPRIT). To allow for a more reliable spectral estimation and to cover nonstationarity in the signal, a sliding-window version of ESPRIT is introduced. This paper describes the basic ESPRIT method as well as sliding-window ESPRIT. The paper discusses the application of the method to one synthetic signal and three measurement signals. It is shown that the method allows for very accurate frequency estimation of interharmonic components. The limitations of the methods, such as line splitting and spurious components, can be overcome by using the coherent information obtained from the sliding-window method. A number of remaining issues are also discussed in this paper. Index Terms--Classification, damped sinusoidal model, estimation of signal parameters via rotational invariance techniques (ESPRIT), features, power quality (PQ), sliding-window ESPRIT, time-frequency analysis of disturbances.

The problem of background modeling for foreground object detection in complex environments is discussed. The test results have shown that the principal features are effective in representing the spectral, spatial and temporal characteristics of the background.

The support vector machine (SVM) is a powerful method for statistical classification of data used in a number of different applications. However, the usefulness of the method in a commercial available system is very much dependent on whether the SVM classifier can be pretrained from a factory since it is not realistic that the SVM classifier must be trained by the customers themselves before it can be used. This paper proposes a novel SVM classification system for voltage disturbances. The performance of the proposed SVM classifier is investigated when the voltage disturbance data used for training and testing originated from different sources. The data used in the experiments were obtained from both real disturbances recorded in two different power net- works and from synthetic data. The experimental results shown high accuracy in classification with training data from one power network and unseen testing data from another. High accuracy was also achieved when the SVM classifier was trained on data from a real power network and test data originated from synthetic data. A lower accuracy resulted when the SVM classifier was trained on synthetic data and test data originated from the power network. [ABSTRACT FROM AUTHOR]

This paper proposes a method for analyzing measurements of voltage transients in three-phase systems. The method is based on the Clarke transform introduced in 1950 for calculations of travelling waves along three-phase transmission lines. The proposed method also shows close similarities with the classification of three-phase unbalanced voltage dips. After extracting the actual transient (e.g., by using a notch filter centered on the power-system frequency), the three signals are decomposed into seven components. From the relation between these seven components, the dominant component is identified. The method is successfully applied to a number of measured transients. The paper also identifies the limitations of the method and gives suggestions for future work. [ABSTRACT FROM AUTHOR]

Presents a novel method which uses a special type of multiscale isotropic bandpass filters to detect image landmark and corner features. Applications for detecting landmarks and corners; Analysis of a sample feature detector; Definition of a family of detectors; Examples in designing feature detection filters.