Wavelet-Ann Based Fault Location Identification in Micro Gridinter Connected Transmission System

Abstract

This paper presents a novel protection scheme for the protection of transmission system with microgrid (MG) having of wind energy, solar PV energy and fuel cell sources. MGs provide environmental, economical benefits for the end consumers, power usages and society. However, transmission line and MGs poses major technical challenges. Protection system must respond both MG and utility grid failures. Technical challenges of MG protection are to respond to main and MG faults. A MG model is designed and it is connected to a transmission line. Later, for detection and classification of faults wavelet Analysis (WT) is used. Faults are detected by the fault indices and compared with defined threshold value. The location of fault is done by artificial neural networks (ANN) on MG connected transmission system using detailed (D1) coefficients of energy current signals. This proposed algorithm is tested and more effective for the detection, classification and location of faults on MG interconnected transmission system. This algorithm is accurate and independent of fault inception angle (FIA), fault impedance and fault distance on line.