Harmonic Disturbance Identification and Suppression Method Under Compound Power Quality Disturbance

Abstract

High photovoltaic penetration introduces coupled power-quality disturbances whose overlapping RMS, harmonic, and transient signatures are difficult to distinguish under noise. This study develops a field-calibrated diagnosis-and-mitigation workflow for distributed photovoltaic systems. A balanced 17-class dataset is synthesized within IEEE Std 1159-2019 phenomenon ranges. The 50 Hz signals are sampled at 2 kHz for 0.2 s, and additive white Gaussian noise is applied. Magnitude and signed S-transform maps form complementary inputs to a two-channel convolutional neural network. Its learned representation and posterior vector are fused with physically interpretable time, frequency, and time-frequency descriptors. A Logistic-map chaos search then tunes a decision-tree ensemble and probability-fusion weights. Under the unified main condition of 20 dB SNR, 200 samples per class, and stratified source-isolated five-fold cross-validation, the method achieves 97.59% ± 0.38% accuracy. Ablation and benchmark tests distinguish the contributions of CNN feature learning, conventional ensemble classification, and chaos-optimized fusion. The diagnostic output is mapped to a harmonic-bearing decision and an order-resolved spectrum descriptor, which guide passive target orders and active-current sharing in an equivalent hybrid active power filter (HAPF) model. Using the same field-calibrated PCC waveform, the HAPF reduces current THD from 6.550% to 0.973% and lowers active-converter RMS current from 60.18 A for APF-only operation to 26.40 A, a 56.1% reduction. The source-isolation rule also prevents leakage between training and testing operating conditions. The workflow therefore links reproducible compound-disturbance diagnosis with lower-capacity harmonic mitigation and is directly relevant to power-quality management in distributed photovoltaic generation.