Wide-Area Monitoring of Power Systems Using Principal Component Analysis and k-Nearest Neighbor Analysis

Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate analysis known as Principal Component Analysis (PCA) and time series analysis known as k-Nearest Neighbor (kNN) analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method

Optimized complex power quality classifier using one vs. rest support vector machine

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a ?One Vs Rest? architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.Fil: de Yong, David Marcelo. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Bhowmik, Sudipto. Nexant Inc; Estados UnidosFil: Magnago, Fernando. Universidad Nacional de Río Cuarto. Facultad de Ingeniería. Departamento de Electricidad y Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentin

Investigation of effective automatic recognition systems of power-quality events

Copyright © 2007 IEEE. All Rights Reserved.There is a need to analyze power-quality (PQ) signals and to extract their distinctive features to take preventative actions in power systems. This paper offers an effective solution to automatically classify PQ signals using Hilbert and Clarke Transforms as new feature extraction techniques. Both techniques accommodate Nearest Neighbor Technique for automatic recognition of PQ events. The Hilbert transform is introduced as single-phase monitoring technique, while with the Clarke Transformation all the three-phases can be monitored simultaneously. The performance of each technique is compared with the most recent techniques (S-Transform and Wavelet Transform) using an extensive number of simulated PQ events that are divided into nine classes. In addition, the paper investigates the optimum selection of number of neighbors to minimize the classification errors in Nearest Neighbor Technique.Gargoom, A.M.; Ertugrul, N.; Soong, W.L

Adapting nearest neighbors-based monitoring methods to irregularly sampled measurements

Prognostics and Health Management Society. All rights reserved.Irregularly spaced measurements are a common quality problem in real data and preclude the use of several feature extraction methods, which were developed for measurements with constant sampling intervals. Feature extraction methods based on nearest neighbors of embedded vectors are an example of such methods. This paper proposes the use of a timebased construction of embedded vectors and a weighted similarity metric within nearest neighbor-based methods in order to extend their applicability to irregularly sampled measurements. The proposed idea is demonstrated within a method of univariate detection of transient or spiky disturbances. The result obtained with an irregularly sampled measurement is benchmarked by the original regularly sampled measurement. Although the method was originally implemented for off-line analysis, the paper also discusses modifications to enable its on-line implementation

Intelligent Methods for Characterization of Electrical Power Quality Signals using Higher Order Statistical Features

This paper considers a few important techniques classification for to identify several power quality disturbances. For this purpose, a process based in HOS has been realized to extract features that help in classification. In this stage the geometrical pattern established via higher-order statistical measurements is obtained, and this pattern is function of the amplitudes and frequencies of the power quality disturbances associated to the 50-Hz power-line. Once the features are managed will be segmented to form training and test sets and them will be applied in the statistical methods used to perform automatic classification of PQ disturbances. The best technique of those compared is selected according to correlation and mistake rates

Voltage Variation Signals Source Identification And Diagnosis Method

Power Quality (PQ) problem has become an important issue for generating bad impact to the users nowadays.It is important to detect and identify the source of the PQ problem. This paper presents a voltage variation signals source identification and diagnosis method by determining the average time frequency representation (TFR) phase power of the impedance. The signals focused in this study are the voltage variation signals, which include voltage sag, swell and interruption. The voltage variation signals from different source location (upstream, downstream as well as up and downstream) according to the IEEE Standard 1159 by using the mathematical models. The signals are first analyzed by using the Spectrograms which act as the feature producing tool. Then, the average power TFR of phase domain of each signal is calculated and tabulated. Finally, the performance of the method is identified by using support vector machine (SVM) and k-nearest neighbor (kNN).The results show that this method is an effective and suitable technique for identifying the source of voltage variatio

Power disturbance monitoring through techniques for novelty detection on wind power and photovoltaic generation

Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.Postprint (published version