@Article{cmes.2026.084732,
AUTHOR = {Hani Albalawi, Rab Nawaz, Abdul Wadood, Anwar Ul Haq, Shahbaz Khan, Bakht Muhammad Khan, Aadel Mohammed Alatwi},
TITLE = {Real-Time Fault Diagnosis in UHV Power Systems Using Bayesian-Optimized 1D CNN on Raw Time-Series Signals},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/28295},
ISSN = {1526-1506},
ABSTRACT = {Ensuring the reliability of Ultra-High Voltage (UHV) power systems remains a critical challenge, as series compensation improves stability while introducing complex fault dynamics. Although machine learning and deep learning methods have advanced fault diagnosis, existing approaches often depend on computationally intensive preprocessing and struggle with data scarcity, limiting real-time applicability. This study proposes a streamlined One-Dimensional Convolutional Neural Network (1D CNN) optimized via Bayesian learning for efficient and robust fault classification in a 735 kV, 32-bus UHV system. The model operates directly on raw time-series signals, eliminating the need for domain-specific transformations while preserving the natural characteristics of the data. Quantitatively, the proposed framework achieves 99.91% test accuracy using combined voltage-current signals, with a narrow standard deviation of <math id="mml-ieqn-1"><mo>±</mo><mn>0.06</mn><mi mathvariant="normal">%</mi></math> across multiple evaluations, and correctly classifies 12,998 out of 13,010 test instances with only twelve misclassifications across eleven fault categories. For resource-constrained applications, voltage-only signals deliver 99.72% accuracy while reducing training time by 38.6% (680 s) and memory usage by 61.8% (1272 MB), whereas current-only signals achieve 99.90% accuracy with the fastest inference speed of 2995 samples per second. The model demonstrates strong dependability under noisy conditions, maintaining 97.03% test accuracy even at 10 dB SNR, and exhibits robustness against downsampling with a Sampling Index of 0.0235. The Bayesian optimization process identifies optimal hyperparameters including a learning rate of 0.002611, dropout rate of 0.182, and batch size of 19. From an economic perspective, the elimination of expensive preprocessing, noise cancellation, and high-frequency sampling (typically 0.2–10 MHz) reduces computational resource requirements and operational costs, enabling practical deployment in real-time monitoring systems. These findings demonstrate the practical potential of the proposed approach for cost-effective, scalable fault diagnosis in large-scale power grids.},
DOI = {10.32604/cmes.2026.084732}
}