Intelligent Sensors for dc Fault Location Scheme Based on Optimized Intelligent Architecture for HVdc Systems

We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg-Marquardt backpropagation (LMBP) method, which multi-stage BO optimizes for efficiency. During training, the feature vectors at the sending terminal of the dc link are selected based on the norm values of the observed waveforms at various frequency bands. The multilayer ANN is trained using a comprehensive set of offline data that takes the denoising scheme into account. This choice not only helps to reduce the computational load but also provides better accuracy. An overall percentage error of 0.5144% is observed for the proposed algorithm when tested against fault resistances ranging from 10 to 485 ohm. The simulation results show that the proposed method can accurately estimate the fault site to a precision of 485 ohm and is more robust.

Automated summary

In this study, a probabilistic model based on local information and a multilayer artificial neural network is proposed to determine the location of dc-link faults in MT-HVdc networks. The model combines discrete wavelet transforms and Bayesian optimization. Through training and optimization, the proposed method accurately estimates the fault site with high robustness.