Classification of Power Quality Disturbances by Using Empirical Mode Decomposition and Support Vector Machine (ME Thesis) (Record no. 58186)

MARC details
000 -LEADER
fixed length control field 02348nam a2200169Ia 4500
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Personal name 15 MPE 03
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Personal name Arslan Memon
-- Supervisor Prof. Dr. Aslam Parvez Memon
245 #0 - TITLE STATEMENT
Title Classification of Power Quality Disturbances by Using Empirical Mode Decomposition and Support Vector Machine (ME Thesis)
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Nawabshah
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher QUEST
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Year of publication 2015
300 ## - PHYSICAL DESCRIPTION
Number of Pages 64
500 ## - GENERAL NOTE
General note ABSTRACT<br/><br/>Now a days, power quality is drastically related with power system issues and disturbance analysis because of the increase of power system events, faults and disturbance rapidly. The problems of power quality occur due to the change in voltage, current and frequency. These disturbances are associated with the interconnected renewable energy generators and to largely non-linear conditions of the load. Therefore it is required to have the knowledge about the nature and type of the power quality disturbances which are well-defined as voltage sags, swells, harmonics and other system interruptions. This education will be beneficial in the reorganization of power quality disturbance. The main aim of this research is to investigate the combination of EMD and SVM, to suggest the best classification method for monitoring of power quality disturbance. This research will focus on the monitoring of power quality disturbances using empirical mode decomposition and support vector machine. These methods are the effective tool for the monitoring of PQD. It is deemed to the binary classification. The methods will combine the disturbance-versus-normal conditions of the system. This research has been done on the basis of modeling parametric mathematical model using classical EMD and combines it with SVM. Feature vector will be helpful in monitoring. The outputs of HHT is the intrinsic mode signals IMFs infamous frequency IF, instantaneous amplitude IA. Characteristics feature are taken from first IMFs. IF and IA. The features i.e. the mean standard deviation singular values, minima and maxima. These will give the power quality disturbance monitoring.by It will be recognize PQD and to diminish it. The proposed methodology has been validated using<br/>MATLAB/Simulink software.<br/><br/><br/><br/><br/><br/><br/><br/><br/>
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Personal name Department of Elecrical Engineering
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Uniform Resource Identifier https://tinyurl.com/bdhdbjkr
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis and Dissertation
Holdings
Withdrawn status Lost status Home library Current library Date acquired Accession Number Koha item type
    Research Section Research Section 27/03/2018 MP/19-182 Thesis and Dissertation
    Research Section Research Section 16/10/2018 MP/32-364 Thesis and Dissertation