Hybrid signal processing and machine intelligence techniques for detection, quantification and classification of power quality disturbances-Reference-Cited by-同舟云学术

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Hybrid signal processing and machine intelligence techniques for detection, quantification and classification of power quality disturbances

Published:2009-04 Issue:3 Volume:22 Page:442-454
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Panigrahi B.K.,Dash P.K.,Reddy J.B.V.

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference34 articles.

1. Fuzzy support vector machines for multiclass problems;Abe,2002

2. A measurement method based on the wavelet transform for power quality analysis;Angrisani;IEEE Transactions on Power Delivery,1998

3. Understanding Power Quality Problems: Voltage Sags and Interruptions;Bollen,1999

4. Learning from Data-—Concepts, Theory and Methods;Cherkassky,1998

5. Support vector networks;Cortes;Machine Learning,1995

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1. Adversarial attack and training for deep neural network based power quality disturbance classification;Engineering Applications of Artificial Intelligence;2024-01

2. Machine Learning Algorithms for the Classification of Time-Frequency Features Revealing Power Quality;2024

3. A Comprehensive Overview on Modified Versions of Stockwell Transform For Power Quality Monitoring;IEEE Access;2022

4. Hybrid signal processing technique to design smart meter for detection of voltage amplitude disturbances;Active Electrical Distribution Network;2022

5. Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules;Energy Engineering;2022

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