Journal ArticleDOI
Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm
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TLDR
A new approach for the detection and classification of single and combined power quality (PQ) disturbances is proposed using fuzzy logic and a particle swarm optimization (PSO) algorithm.About:
This article is published in Electric Power Systems Research.The article was published on 2010-12-01. It has received 120 citations till now. The article focuses on the topics: Membership function & Fuzzy logic.read more
Citations
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Journal ArticleDOI
A critical review of detection and classification of power quality events
TL;DR: A comprehensive review of signal processing and intelligent techniques for automatic classification of the power quality (PQ) events and an effect of noise on detection and classification of disturbances is presented in this paper.
Journal ArticleDOI
A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network
Shouxiang Wang,Haiwen Chen +1 more
TL;DR: Comparisons with other state-of-the-art deep neural networks and traditional methods proves that the proposed method can overcome defects of traditional signal process and artificial feature selection.
Journal ArticleDOI
A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances
TL;DR: A comprehensive literature review on the applications of digital signal processing, artificial intelligence and optimization techniques in the classification of PQ disturbances and a comparison of various classification systems is presented in tabular form.
Journal ArticleDOI
Classification of power quality events – A review
Manish Kumar Saini,Rajiv Kapoor +1 more
TL;DR: This paper carries out a comprehensive review of articles that involves a comprehensive study of signal processing techniques used for PQ analysis and intelligent techniques such as fuzzy logic, neural network and genetic algorithm as well as their fusion are reviewed.
Journal ArticleDOI
Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals
TL;DR: Good results were obtained that corroborate the hypothesis that the feature extraction step is necessary to classify disturbances effectively and with low computational effort.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book ChapterDOI
Comparison between Genetic Algorithms and Particle Swarm Optimization
Russell C. Eberhart,Yuhui Shi +1 more
TL;DR: This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization, and suggests ways in which performance might be improved by incorporating features from one paradigm into the other.
Book
Introduction to Fuzzy Logic using MATLAB
TL;DR: This paper presents a model for a Fuzzy Rule-Based System that automates the very labor-intensive and therefore time-heavy process of decision-making in the context of classical sets.
Book
High Voltage Engineering
M S Naidu,V Kamaraju +1 more
TL;DR: Conduction and breakdown conduction and breakdown in liquid dielectric breakdown in solid dielectrics applications of insulating materials generation of high voltages and currents meaurement of high voltage and currents overvoltage phenomenon and insulation co-ordination in electric power systems non-destructive testing of materials and electrical apparatus high voltage testing of electrical apparatus design, planning and layout of high-voltage laboratories as discussed by the authors.
Journal ArticleDOI
An effective wavelet-based feature extraction method for classification of power quality disturbance signals
TL;DR: A wavelet norm entropy-based effective feature extraction method for power quality (PQ) disturbance classification problem and a classification algorithm composed of a wavelet feature extractor based on norm entropy and a classifier based on a multi-layer perceptron are presented.