Wavelet-ANN Based Detection of Fault Location of Hybrid Renewable Energy Sources Connected Power Transmission System

savarapu chandra shekar, T Muthamizhan, Mohammad Aijaz, D Chandra Sekhar

Abstract


The complexity of the power system increases as the hundreds of lines involved due to the penetration of conventional and renewable energy sources to meet the increased load demand. Transmitting the power for hundreds of kilometres long distances makes complexity in the network to locate the fault. Thus it is necessary to develop suitable algorithms to identify the location of the fault accurately in presence of large number of transmission lines. In this paper a novel Wavelet Artificial Neural Network (WANN) based method is developed where the Detailed coefficients (D1 coefficients) obtained from the current signals are used for training and testing ANN. The fault location is carried out in presence of renewable energy sources for various distances, fault impedances on 4-bus connected transmission system. A 4-bus transmission system is simulated using simulation software and the analysis of fault is done by using current signals of various faults with the help of wavelet multi-resolution analysis at both buses. This analysis is worked out almost within half cycle. The proposed wavelet based algorithm is tested for all fault conditions in presence of renewable energy sources with different power ratings at various distances and hence it is proved that the proposed method provided the best results for different fault impedances, fault generator capacities, fault inception angles (FIA).


Keywords


D1 coefficients, Wavelet-ANN, Bior1.5 mother wavelet, fault inception angle, fault impedance

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v14i3.14396.g8921

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