Wavelet-ANN Based Analysis of PV-IoT Integrated Two Area Power System Network Protection in presence of SVC

Abstract

Power system network is one of the most widely used in engineering system in the field of electrical field for moving bulk amount of power from one location to the other several directions of the country over thousands of kilometres. Integration of power projects typically involves adding new energy sources like wind, solar generation units with shunt and series compensating devices to an existing power system network. It is essential to design new protection scheme due to changes in the topology and dynamic behaviour of the system. Now fast fault detection algorithmic approaches are necessary to integrate different types of generating sources and loads under smart environment. The protection scheme must provide physical monitoring as well as para-metrical with the help new technologies. Internet-of-things(IoT) is one of the source to monitor electrical systems under various environmental conditions of the system. Wavelet (WT) basically investigates the fault transient signals of different frequency and divides the waveform into different approximate and detailed coefficient values, which provides the important knowledge about the classification and location of fault. The detection of faulty-line and the location of fault by implementation wavelet detailed coefficients of Bior1.5 mother wavelet and for location of transmission line fault using Artificial neural network technique. This proposed method provide Condition monitoring Analysis of IoT based Wind-SVC Integrated Two Area Power System Network Protection scheme using artificial neural networks and wavelet approach under various types of faults.