TY  - BOOK
AU  - 15 MPE 03
AU  - Arslan Memon 
AU  - Department of Elecrical Engineering
TI  - Classification of Power Quality Disturbances by Using Empirical Mode Decomposition and Support Vector Machine (ME Thesis)
CY  - Nawabshah
N1  - ABSTRACT

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
MATLAB/Simulink software.









UR  - https://tinyurl.com/bdhdbjkr
ER  -