Integral mathematical model of power quality disturbances

Power quality (PQ) disturbances lead to severe problems in industries and electrical grids. To mitigate PQ problems, the accurate detection and classification of the possible disturbances are essential. A large number of studies exists in this field. The first research step in these studies is to obtain several distorted signals to test the classification systems. In this regard, the most common trend is the generation of signals from mathematical models. In the literature, we can find several models with significant differences among them. However, to the best of our knowledge, there is no integral model that considers all types of distortions. This work presents an integral mathematical model based on the models found in the literature. The model also includes new types of combined disturbances. Twenty-nine disturbances are considered. Additionally, this work includes a software version of this integral model that is publicly available to be used by any interested researcher. In this way, PQ disturbances can be generated in a fast and automatic way. This software aims to facilitate future studies, supporting researchers in the modelling stage

A Novel Methodology for Power Quality Disturbances Detection and Classification in Industrial Facilities

819-823The industrial facilities inject noise to the power line. Concerning this issue, researchers are focusing their effort on developing new techniques for analyzing the power quality of the power net. This work presents a novel methodology for power quality disturbances detection and classification based on the Harris hawks optimization algorithm and discrete wavelet transforms decomposition of the signal

A Novel Methodology for Power Quality Disturbances Detection and Classification in Industrial Facilities

The industrial facilities inject noise to the power line. Concerning this issue, researchers are focusing their effort on developing new techniques for analyzing the power quality of the power net. This work presents a novel methodology for power quality disturbances detection and classification based on the Harris hawks optimization algorithm and discrete wavelet transforms decomposition of the signal

Automatic classification of power quality disturbances: a review

The development of intelligent power quality (PQ) disturbances classification and analysis tools exploited various digital signal-processing techniques to extract important features from the PQ signals. The purpose of this paper is to present a comprehensive review and discussion of the advanced tools for the automatic classification of PQ disturbances. The digital signal-processing tools applied for feature extraction include Fourier-transform, Wavelet-transform, Stockwell-transform etc. For the classification of PQ disturbances, the artificial intelligence techniques such as artificial neural networks, fuzzy logic and support vector machine are reviewed here. A large number of features used as inputs to the classifiers may affect the accuracy rate and requires a large memory space. The optimization techniques have been used in literature for optimal feature selection, which include genetic algorithm, simulated annealing, particle swarm optimization and ant colony optimization. An extensive review provides to the researchers a clear perspective on various techniques of PQ disturbances classification

Automatic classification of power quality disturbances using optimal feature selection based algorithm

The development of renewable energy sources and power electronic converters in conventional power systems leads to Power Quality (PQ) disturbances. This research aims at automatic detection and classification of single and multiple PQ disturbances using a novel optimal feature selection based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). DWT is used for the extraction of useful features, which are used to distinguish among different PQ disturbances by an ANN classifier. The performance of the classifier solely depends on the feature vector used for the training. Therefore, this research is required for the constructive feature selection based classification system. In this study, an Artificial Bee Colony based Probabilistic Neural Network (ABCPNN) algorithm has been proposed for optimal feature selection. The most common types of single PQ disturbances include sag, swell, interruption, harmonics, oscillatory and impulsive transients, flicker, notch and spikes. Moreover, multiple disturbances consisting of combination of two disturbances are also considered. The DWT with multi-resolution analysis has been applied to decompose the PQ disturbance waveforms into detail and approximation coefficients at level eight using Daubechies wavelet family. Various types of statistical parameters of all the detail and approximation coefficients have been analysed for feature extraction, out of which the optimal features have been selected using ABC algorithm. The performance of the proposed algorithm has been analysed with different architectures of ANN such as multilayer perceptron and radial basis function neural network. The PNN has been found to be the most suitable classifier. The proposed algorithm is tested for both PQ disturbances obtained from the parametric equations and typical power distribution system models using MATLAB/Simulink and PSCAD/EMTDC. The PQ disturbances with uniformly distributed noise ranging from 20 to 50 dB have also been analysed. The experimental results show that the proposed ABC-PNN based approach is capable of efficiently eliminating unnecessary features to improve the accuracy and performance of the classifier

Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines

In many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for quality assessment (QA) in manufacturing industries. In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set. Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided

Detección, clasificación y localización de eventos de calidad de energía utilizando técnicas avanzadas de procesamiento de señales e inteligencia artificial

La convergencia de la red eléctrica tradicional con los sistemas de generación distribuida, las fuentes de energía renovable y la incorporación de dispositivos electrónicos no lineales, generan una gran cantidad de fenómenos electromagnéticos que provocan un deterioro en la calidad de la energía consumida por el usuario.La desregulación de los mercados y el hecho que los usuarios residenciales e industriales se hayan vuelto más exigentes en lo referente a la calidad de la energía que consumen genera marcadas presiones a todos los actores del mercado para brindar una energía de mayor calidad.Por este motivo resulta imprescindible que las empresas eléctricas cuenten con herramientas que permitan el monitoreo del estado de la red en tiempo real para gestionar adecuadamente tareas de mantenimiento preventivo y reactivo de la red y, de este modo, mejorar los índices que miden la calidad de la energía.En el futuro, las empresas prestadoras de energía eléctrica se verán obligadas a incurrir en importantes gastos para implementar dispositivos de medición, redes de comunicación para centralizar la información y algoritmos de procesamiento que permitan extraer información importante de los datos colectados.Sin importar los objetivos técnicos involucrados en la implementación de un sistema de monitoreo el principal motivo que fundamenta su implementación radica en la necesidad, de mitigar las pérdidas económicas generadas por las perturbaciones en suministro eléctrico.Un algoritmo de clasificación de perturbaciones y localización de fallas tiene por objetivo principal encontrar la fuente de la perturbación para establecer diagnósticos de la salud de la red y, en caso de ser necesario, rápidamente restablecer el servicio.En este contexto, en la presente tesis se presentan algoritmos innovadores de procesamiento de mediciones obtenidas en un sistema eléctrico con el objetivo de detectar, caracterizar, clasificar y localizar perturbaciones en un sistema eléctrico de potencia, empleando la menor cantidad posible de información.La mayoría de los algoritmos propuestos por la comunidad científica se enfocan a detectar y clasificar una perturbación, dentro de un conjunto de muestras, evitando abordar casos que comúnmente se presentan en la realidad tal como las perturbaciones complejas.Por otro lado, la mayoría de las publicaciones relativas a la localización de fallas se enfocan en sistemas eléctricos de transmisión siendo muy escasos los trabajos que abordan la problemática desde el punto de vista de un sistema de distribución.En la presente tesis se presenta el desarrollo de diversos algoritmos cuyo objetivo radica en la detección y clasificación de perturbaciones simples y complejas así como también en la localización de fallas en sistemas eléctricos de potencia.Los desarrollos realizados están fundamentados en avanzadas técnicas de procesamiento de señales, diversas estrategias para la extracción de parámetros característicos, métodos de minería de datos para la selección de parámetros característicos y técnicas innovadoras de Inteligencia Artificial para la clasificación y localización de eventos de calidad de energía.Los métodos desarrollados presentan destacados resultados en comparación con las publicaciones que abordan la problemática propuesta en la presente tesis. Por este motivo, los algoritmos desarrollados se enfocarán en la detección y clasificación de perturbaciones complejas y en la localización de fallas en sistemas eléctricos de distribución.Fil: de Yong, David Marcelo. Autor; . 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