Support Vector Regression Based S-transform for Prediction of Single and Multiple Power Quality Disturbances

This paper presents a novel approach using Support Vector Regression (SVR) based S-transform to predict the classes of single and multiple power quality disturbances in a three-phase industrial power system. Most of the power quality disturbances recorded in an industrial power system are non-stationary and comprise of multiple power quality disturbances that coexist together for only a short duration in time due to the contribution of the network impedances and types of customers’ connected loads. The ability to detect and predict all the types of power quality disturbances encrypted in a voltage signal is vital in the analyses on the causes of the power quality disturbances and in the identification of incipient fault in the networks. In this paper, the performances of two types of SVR based S-transform, the non-linear radial basis function (RBF) SVR based S-transform and the multilayer perceptron (MLP) SVR based S-transform, were compared for their abilities in making prediction for the classes of single and multiple power quality disturbances. The results for the analyses of 651 numbers of single and multiple voltage disturbances gave prediction accuracies of 86.1% (MLP SVR) and 93.9% (RBF SVR) respectively. Keywords: Power Quality, Power Quality Prediction, S-transform, SVM, SV

Advances in power quality analysis techniques for electrical machines and drives: a review

The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.Peer ReviewedPostprint (published version

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

Detection of Anomalies in the Quality of Electricity Supply

From the last two decades, power quality is getting much attention. Proper functioning of the equipment depends upon the quality of power supplied. Every year, demand of electric power goes on increasing and the power system network is expanding and becoming more complex. On account of thrust on clean power supply, use of renewable sources has dramatically increased in grid but it simultaneously causes power quality problems. In this work, power quality disturbance detection in wind farm integrated with grid is presented. For disturbance detection, time-time transform has been employed. The disturbance signal for the detection purpose is generated in MATLAB/Simulink environment by using a Simulink model

A comparison framework for distribution system outage and fault location methods

Finding the location of faults in distribution networks has been a long standing problem for utility operators, and an interesting subject for researchers as well. In recent years, significant research efforts have been devoted to the development of methods for identification of the faulted area to assist utility operators in expediting service restoration, and consequently reducing outage time and relevant costs. Considering today's wide variety of distribution systems, a solution preferred for a specific system might be impractical for another one. This paper provides a comparison framework which classifies and reviews a relatively large number of different fault location and outage area location methods to serve as a guide to power system engineers and researchers to choose the best option based on their existing system and requirements. It also supports investigations on the challenging and unsolved problems to realize the fields of future studies and improvements. For each class of methods, a short description of the main idea and methodology is presented. Then, all the methods are discussed in detail presenting the key points, advantages, limitations, and requirements

Detection of Grid Voltage Anomalies via Broadband Subspace Decomposition

Due to the increase of sensitive loads on the mains power grid, measurement and monitoring of the power quality (PQ) have become an important factor for both consumers and operators. As is well-known, PQ problems occur in a very short time period with specific characteristics. In transmission or distribution systems, power quality data are collected from monitoring devices such as digital fault recorders, power quality and dynamic system monitors, etc. The recorded data has to be analysed in order to understand system anomalies. These anomalies may be due to sources of broadband noise. In this study, we employ broadband subspace decomposition, using polynomial eigenvalue decomposition, to detect these anomalies. Results demonstrate that this method may be considered as a new and effective tool for measurement and monitoring of PQ problems

Power quality disturbance detection based on mathematical morphology and fractal technique

Based on mathematical morphology and grille fractal, a novel approach on power quality disturbance detection and location is presented in this paper. At first a parallel composite morphological filter with multiple-structure elements is designed to filter the random noise and impulse noise in power quality disturbance signals. Then to the filtered curves, an easy implementation criterion for singularity detection based on the change regularity of grille fractal is proposed to locate the start and end time that disturbance occurs. The voltage sag, swell, harmonic and their combined disturbances are used to verify the validity of the proposed filter-location approach. Numerical results show that the proposed approach is valid and effective. © 2005 IEEE.published_or_final_versio

ANALYSIS OF POWER QUALITY EVENTS USING WAVELETS

Wavelets are prominently used for Power Quality (PQ) signal analysis, the features that are computed from wavelet sub bands are informative for detection and classification. Energy levels of non-stationary events that occur in PQ signal computed considering wavelet sub bands suffer from shift variant property and hence use of dual tree complex wavelets that supports shift invariance property is used for PQ event analysis. In this paper, PQ event algorithm is developed considering dual tree wavelets and the results are compared with wavelets. Various PQ signals with non-stationary events are analyzed and the shift invariant property of dual tree wavelets is demonstrated to be advantageous in terms of event classification. Dual Tree Complex wavelet Transform (DTCWT) energy levels are capable of differentiating between multiple events as well as different types of sags, swells, harmonics, interrupts and flicker. The classification accuracy using DTCWT energy bands is improved by more than 90%. DTCWT filters selected in this paper are suitable for PQ event detection as well as classification