Journal ArticleDOI
Power quality recognition in distribution system with solar energy penetration using S-transform and Fuzzy C-means clustering
TLDR
In this paper, the authors presented a technique to recognize the power quality disturbances associated with solar energy penetration in distribution network using a standard IEEE-13 bus test system modified by incorporating the solar PV system.About:
This article is published in Renewable Energy.The article was published on 2017-06-01. It has received 83 citations till now. The article focuses on the topics: Photovoltaic system & Solar energy.read more
Citations
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Journal ArticleDOI
Power Quality Classification of disturbances using Discrete Wavelet Packet Transform (DWPT) with Adaptive Neuro-Fuzzy System
TL;DR: The results show that the proposed method has better performance compared with the existing classification methods and provides more accuracy when compared to the existing systems such as Discrete Wavelet Transform based Fuzzy Logic Adaptive System and Fourier Transform based Artificial neural networks.
Journal ArticleDOI
Classification Method of Voltage Sag Sources Based on Sequential Trajectory Feature Learning Algorithm
TL;DR: An effective and interpretable voltage sag sources classification method based on sequential trajectory feature learning and Random Forest algorithm is proposed and results show that the proposed method has significant advantages in terms of accuracy and interpretability of voltage Sag sources classification.
Journal ArticleDOI
Classification Method of Voltage Sag Sources Based on Sequential Trajectory Feature Learning Algorithm
TL;DR: Wang et al. as discussed by the authors proposed an effective and interpretable voltage sag sources classification method based on sequential trajectory feature learning and random forest algorithm, which inherits the interpretability of shapelet.
Proceedings ArticleDOI
S-Transform Based Protection Scheme for Distribution System Integrated with Solar Power Plant
TL;DR: Stockwell transform based protection scheme has been proposed to detect and classify faults in a distribution network integrated with a solar photo voltaic power plant (SPV) and the immunity of the proposed algorithm to switching transients, energization/de-energization of power components and noisy environment has been established.
Journal ArticleDOI
Classification of Power Quality Disturbances in Solar PV Integrated Power System Based on a Hybrid Deep Learning Approach
Belkis Eristi,Huseyin Eristi +1 more
TL;DR: It has been found that the proposed hybrid deep learning approach has the ability to accurately recognize the PQDs even if the SPV plant integrated power system has a negative effect on power quality.
References
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Journal ArticleDOI
Localization of the complex spectrum: the S transform
TL;DR: The S transform is shown to have some desirable characteristics that are absent in the continuous wavelet transform, and provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum.
Journal Article
Localisation of the complex spectrum : The S transform
TL;DR: The S transform as discussed by the authors is an extension to the ideas of the Gabor transform and the Wavelet transform, based on a moving and scalable localising Gaussian window and is shown here to have characteristics that are superior to either of the transforms.
Journal ArticleDOI
Radial distribution test feeders
TL;DR: In this paper, the authors present an updated version of the same test feeders along with a simple system that can be used to test three-phase transformer models, which is a common set of data that could be used by program developers and users to verify the correctness of their solutions.
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
Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network
TL;DR: The simulation results reveal that the combination of S-Transform and PNN can effectively detect and classify different PQ events and it is found that the classification performance of PNN is better than both FFML and LVQ.