The increasing penetration of intermittent, non-synchronous generation has led to a reduction in total power system inertia. Low inertia systems are more sensitive sudden changes and susceptible secondary issues that can result large-scale events. Due the short time frames involved, automatic methods for event detection diagnosis required. Wide-area monitoring (WAMS) provide data required detec...

Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases...

This paper presents a new approach for the classification of the power systemdisturbances using support vector machines (SVMs). The proposed approach is carried out at three serial stages. Firstly, the features to be form the SVM classifier are obtained by using the wavelet transform and a few different feature extraction techniques. Secondly, the features exposing the best classification accur...

Wind power prediction (WPP) of wind farm clusters is important to the safe operation and economic dispatch system, but it faces two challenges: (1) The dimensions input parameters for WPP are very high so that contain irrelevant or redundant features; (2) difficult build a holistic model with high-dimensional clusters. To overcome these challenges, novel short-term clusters, based on sequential...

Recent advances in sensor technology opened new possibilities for remote sensing. For example, the appearance of sensor higher spatial and spectral resolution. In terms of spectral resolution, the number of available bands increased significantly, resulting in hyperspectral sensors. Hyperspectral remote sensing images are characterized by the division of the electromagnetic spectrum in a great ...

In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...

In this paper, we introduce the basic concepts of some state-of-the-art classification methods, including independent component analysis (ICA), principal component analysis (PCA), Bayes method, and support vector machine (SVM) or kernel machine. We discuss their function in the classification and evaluate their performance for different applications. 1 STATISTICAL CLASSIFICATION Classification ...

The Ensemble and mixture of expertise method is the most intuitive simple way to improve performance in field recognition using convolutional neural networks (CNNs). However, difficult apply real-time operation applications because amount computational overhead parameters increase proportion number models. In another, a that extracts various combines it cumbersome requires large change network....

We present and study the Contribution-Selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the Multiperturbation Shapley Analysis, a framework which relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. Empirical co...

We present and examine a novel Contribution-Selection algorithm (CSA) for feature selection based on the Multi-perturbation Shapley Analysis. The algorithm combines both the filter and wrapper approaches in a multi-phasic manner to estimate features’ usefulness and select them accordingly, either using forward selection or backward elimination. Empirical comparison of several feature selection ...