POA-Optimized 1D-CNN with Channel Attention for Power Quality Disturbance Classification Under Strong Noise Conditions

Power quality disturbance (PQD) classifiers can lose 30–50 percentage points of accuracy when the signal-to-noise ratio (SNR) drops to 5 dB. This study finds that the degradation arises primarily not from architectural limitations but from hyperparameter configurations that fail to adapt across noise levels, within the IEEE 1159 disturbance set and additive-noise conditions studied here. Building on this insight, we propose a one-dimensional convolutional neural network (1D-CNN) with channel attention, whose three key hyperparameters—the learning rate, the first-layer convolutional kernel size, and the attention reduction ratio—are automatically optimized using the Phong Optimization Algorithm (POA). A 12-class synthetic dataset constructed to IEEE 1159-2019, with noise levels from noise-free to 5 dB SNR, is used for training and evaluation. Under the extreme condition of 5 dB SNR, the proposed method achieves 83.4% accuracy, outperforming a support vector machine (SVM) with discrete wavelet transform features (66.30%), a hybrid CNN–long short-term memory network (CNN-LSTM; 43.32%), and plain 1D-CNN (38.39%) under their commonly reported configurations. When every deep-learning baseline receives the same POA hyperparameter optimization under a fair per-SNR protocol, this advantage largely disappears: at 5 dB SNR all POA-optimized deep methods fall within roughly 5 percentage points (82.31–87.13%), and the attention module’s own contribution shrinks to within run-to-run variation (the same proposed model scoring 82.77% with attention vs. 82.48% without), showing that systematic hyperparameter optimization, not architectural novelty, drives the noise robustness. A single-set ablation under the original 10 dB-optimized configuration points the same way (POA optimization alone raising 5 dB accuracy from 41.6% to 76.4%, with channel attention adding a further 7 points). A comparison against classical threshold-index classifiers on the identical dataset shows the same pattern at the level of hand-crafted features: with fixed clean-calibrated thresholds, the index classifier collapses from 95.7% to 17.5% at 5 dB SNR, while per-noise-level re-calibration of the same indices recovers 83.8%. Furthermore, the POA-optimized hyperparameters were validated on an independent public PQD dataset, achieving 90.00% accuracy when training a fresh model from scratch. It also stays robust under more realistic complex noise (80.9% at 5 dB SNR) and, on a two-class real-measured probe, transfers to field signals with only light calibration. These findings suggest that for noise-robust PQD classification, hyperparameter optimization deserves as much attention as the architectural design itself, rather than being treated as a final tuning step.