Adaptive Denoising of Partial Discharge Using Absolute Difference Optimization Versus Artificial Neural Networks

Accurate partial discharge (PD) localization in medium-voltage (MV) power cables is essential for condition-based maintenance, yet it remains unreliable when PD pulses are masked by broadband noise and narrowband interference. The novelty of this work is a controlled denoiser-to-localization benchmarking framework that isolates the denoising front end, while keeping the downstream PD detection and localization backend fixed, allowing localization differences to be attributed solely to denoising decisions. Within this fixed-backend paradigm, an optimization-driven Adaptive Denoising Optimization (ADO) method is introduced as an adaptive discrete wavelet transform (DWT) front end that systematically selects the mother wavelet, decomposition level, and threshold parameters to preserve time-of-arrival (ToA) critical wavefront features rather than only maximizing noise suppression. ADO is evaluated against two learning-based denoisers, a multilayer artificial neural network (ANN) and a lightweight feedforward neural network (FNN), using MATLAB simulations of synthetic PD pulses corrupted by white Gaussian noise (WGN) and discrete spectral interference (DSI) over SNRs from 9.78 dB to -10.34 dB. Performance is quantified using execution time, percentage localization error (PE), median absolute localization error (MedAE), and F1 score. Results show that ADO delivers the most robust localization fidelity, maintaining near-zero PE above -6 dB, keeping PE below 0.3% at -10.34 dB, achieving sub-metre MedAE, and sustaining F1 close to 1.0 across noise levels. In contrast, FNN is the fastest option, reducing runtime by approximately 15% versus ANN and 27% versus ADO, highlighting a practical robustness-efficiency trade-off for real-time MV cable monitoring.

@article{Chin2025,
  title     = {Adaptive Denoising of Partial Discharge Using Absolute Difference Optimization Versus Artificial Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Kui-Fern Chin and Chang-Yii Chai and Ismail Saad and Yee-Ann Lee},
  doi       = {10.14569/IJACSA.2025.0161257},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161257}
}