Protection for 330 kV transmission line and recommendation for Nigerian transmission system: a review

Vincent Nsed Ogar, Kelum A. A. Gamage, Sadjjad Hussain


The transmission line is an integral part of the electrical power system; however, a fault has a negative impact on the system, like blackout, power loss, financial losses, and socio-economic impact. This fault occurs due to ageing conductors, lightning stroke, switching surge and human interference. We reviewed the protection scheme implemented in the Nigerian transmission network, which has challenges relating to the environment's terrain and a long-distance transmission line of about 20,000 km. The different approach of fault classification, detection and location was analysed and critically summarised. This review paper proposes a hybrid Artificial Neural Network and distance protection scheme that can automatically identify, locate, isolate, predict, correct faults, and real-time monitor and control the entire network. It can also detect the shortest possible trip time of 0.02 s and 0.03 s of line current and fault losses, respectively, during fault to avert damage on the line. However, this method has its challenges, such as the volume of data generated from load flow analysis, training time, and the total distance covered by the network. However, these can be averted by segmenting the entire network for easy evaluation and monitoring to achieve set goals.


artificial neural network; fault classification; fault identification; fuzzy logic; transmission line; wavelet approach;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578