The scientific research paradigm was adopted for the study. The study uses the transient signal generated during fault conditions to identify faults. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components.
This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. Protection schemes are usually implemented in the planning of transmission line operations.
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