Proceedings ArticleDOI
Recognition of power quality disturbances using S-transform and rule-based decision tree
Om Prakash Mahela,Abdul Gafoor Shaik +1 more
- pp 1-6
TLDR
In this article, a method for recognition of power quality disturbances using Stockwell's transform has been presented, which includes voltage sag, swell, interruption, harmonics, notch, flicker, oscillatory transient, impulsive transient and spike.Abstract:
This paper presents a method for recognition of power quality disturbances using Stockwell's transform. Power quality disturbances are generated using MATLAB as per IEEE standards. Various features of signals are extracted from the multi-resolution analysis based on Stockwell's transform. These features are used to classify various power quality disturbances using the rule-based decision tree. It is observed that high efficiency of classification is achieved using S-transform based ruled decision tree. The investigated power quality disturbances include voltage sag, swell, interruption, harmonics, notch, flicker, oscillatory transient, impulsive transient and spike. Effectiveness of the proposed algorithm has been established by satisfactory results of various case studies.read more
Citations
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Journal ArticleDOI
Fast TT transform and optimized probabilistic neural network-based power quality event detection and classification
Journal ArticleDOI
Identification of Power Quality Transient Disturbance Based on S Transform and Wavelet Transform
TL;DR: The experimental results show that the power quality transient disturbance classification method proposed can accurately identify the type of disturbance and have strong robustness, and fully meet the requirements of the relevant industry for the accuracy of powerquality transient disturbance recognition.
Proceedings ArticleDOI
Research on Identification Method of Voltage Sag Type Based on MI-CNN
TL;DR: Wang et al. as mentioned in this paper proposed a voltage sag type identification method based on multi-input convolutional neural network (MI-CNN), in which the voltage and current data are input into the two input layers of MI-CNN respectively, and the features of each data feature are extracted separately.
Proceedings ArticleDOI
Soft computing approach for classification of complex power quality events
TL;DR: In this article, a bagged tree classifier is used to identify individual and multiple power quality disorders in electrical power systems, and the results obtained indicate that the suggested classification model would be used effectively to identify individuals and multiple Power Quality disorders.
References
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Journal ArticleDOI
Power quality assessment via wavelet transform analysis
TL;DR: In this article, the authors present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances using dyadic-orthonormal wavelet transform analysis.
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
Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks
TL;DR: In this article, a novel approach for detection and classification of power quality (PQ) disturbances is proposed, where distorted waveforms are generated based on the IEEE 1159 standard, captured with a sampling rate of 20 kHz and de-noised using discrete wavelet transform (DWT) to obtain signals with higher signal-to-noise ratio.
Journal ArticleDOI
Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree
TL;DR: In this article, an algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed for the recognition of single stage and multiple power quality (PQ) disturbances.
Journal ArticleDOI
Islanding and Power Quality Disturbance Detection in Grid-Connected Hybrid Power System Using Wavelet and $S$ -Transform
TL;DR: Comparison study between wavelet transform (WT) and S-transform (ST) based on extracted features for detection of islanding and power quality (PQ) disturbances in hybrid distributed generation (DG) system demonstrates the advantages of S -transform over WT in detection of Islanding and different disturbances under noise-free as well as noisy scenarios.