An intelligent overcurrent relay to protect transmission lines based on artificial neural network

Noha Abed-Al-Bary Al-Jawady, Mohammed Ahmed Ibrahim, Laith A. Khalaf, Mahmood Natiq Abed


Power systems are susceptible to faults due to system failures or natural calamities. This could be caused by damage to power system components, resulting in an interruption of power delivery to clients. Overcurrent relays are important relays that protect distribution feeders, transmission lines, transformers, and other components. The intelligent relay can perform both primary and secondary functions. Line-to-ground (L-G) faults are the most common occurrence in long transmission lines, posing a serious threat to electrical equipment. This article presents improved fault classification for transmission line overcurrent protection and highlights the use of artificial neural network (ANN) techniques to protect transmission lines of 100 km (terco type). An ANN is used to classify the faults. A back propagation neural network (BPNN) is used in this case. The neural network has been trained to classify faults in transmission lines for overcurrent protection. Various fault conditions are considered. In the event of a fault condition, the output of a neural network will be a tripping signal. The MATLAB neural network tool and the Simulink package are used to model the suggested method.


ANN; fault condition; MATLAB/Simulink; overcurrent relay; transmission line protection

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