Wavelet Transform and Artificial Neural Network-based Fault Detection and Classification for Nigerian 330 kV Transmission Network

Abstract

Reliable fault detection and classification are essential for improving the security and operational stability of transmission networks. However, existing techniques often depend on effective feature extraction and may experience reduced performance under varying fault conditions. This study presents an intelligent fault detection and classification approach for the Nigerian 330 kV transmission network using the Wavelet Transform (WT) and Artificial Neural Network (ANN). The transmission system was modelled in MATLAB/Simulink R2021b, in which twelve fault conditions were simulated at different fault locations and fault resistances. The Daubechies-4 (db4) wavelet was employed to extract maximum current coefficients, which served as input features for ANN training and testing. A total of 372 fault and no-fault datasets were generated, with 279, 37, and 56 samples used for training, testing, and validation, respectively. The fault detection model (4-10-1-1) achieved a best validation mean squared error (MSE) of 2.5579 × 10⁻¹¹ with a regression coefficient of R = 1.0000, while the fault classification model (4-20-4-4) produced a best validation MSE of 2.3021 × 10⁻⁴ and regression coefficients of 0.99945, 0.99905, and 0.99953 for the training, testing, and validation datasets, respectively. These results demonstrate that the proposed WT-ANN framework provides fast, accurate, and reliable fault detection and classification, making it a promising intelligent technique for modern transmission line protection.