Electromagnetic interference (EMI) from highvoltage
power systems can significantly jeopardize adjacent
conductive structures, such as trains, communication lines, and
pipelines, potentially undermining system integrity and
operational safety. Accurately predicting the degree of induced
voltage is crucial for designing effective mitigation systems for
metallic pipelines. Researchers can estimate electromagnetic fields
(EMF) with remarkable accuracy in a brief period of time using
Artificial Intelligence (AI) techniques. Three Artificial Neural
Network (ANN) models are introduced in this paper that were
created to estimate the voltage induced by AC power from
Overhead Transmission Lines (OHTLs) in pipelines. The models
were trained on a dataset of pipeline-induced voltage
measurements and OHTLs parameters to predict the induced
voltage based on these features. The models include a Feed-
Forward Neural Network (FFNN), a Radial Basis Function Neural
Network (RBFNN), and a Hybrid Neural Network (HNN). A
sensitivity analysis was performed on the hyper-parameters of
these models to identify the ideal configuration for enhanced
accuracy and response time. The HNN model significantly
outperformed FFNN and RBFNN in predicting pipeline-induced
voltage, demonstrating an impressive decrease of 90.92848% and
56.59041% within Root-Mean-Squared Errors (RMSE),
respectively. This makes HNN model a promising choice for
pipeline-induced voltage prediction, outperforming methods
proposed in other recent studies. After training, the model is tested
with a separate dataset, and its accuracy and speed for new data
points are evaluated. The model can predict induced voltage with
nearly 97.15% accuracy within 15 milliseconds. These results
show that the hybrid method outperforms existing AI-based
techniques, with sensitivity analysis revealing that HNN models
with hidden layers of triple and double are the most effective for
pipeline-induced voltage prediction |
