PGNN-Based Harmonic Source Localization in Distribution Networks

Abstract

Accurate localization and quantification of multiple harmonic sources from limited synchronized measurements remain challenging in distribution networks. To address measurement noise, limited phasor measurement unit (PMU) deployment, and insufficient physical consistency in purely data-driven models, this paper proposes a physics-guided neural network (PGNN) incorporating bus-injection-to-branch-current (BIBC) constraints. The method combines the branch harmonic-current phasors available under the PMU observability configuration with a minimum-norm nodal-current prior obtained using the pseudoinverse of the corresponding observable BIBC submatrix. A shared convolutional feature extractor with classification and regression heads jointly estimates source locations and nodal harmonic injection currents. A BIBC-based branch-current consistency term penalizes discrepancies between reconstructed and available branch currents. Simulations on the modified IEEE 13-bus and modified IEEE 33-bus systems demonstrate that, under complete branch-current observability and a nominal noise level of 5%, PGNN achieves higher exact-match accuracy (EM) and micro-averaged F1-scores than the convolutional neural network (CNN), fully connected neural network (FCNN), and graph convolutional network (GCN) baselines for the 3rd, 5th, 7th, and 11th harmonics. Under these conditions, PGNN also yields lower current-magnitude percentage errors and phase-angle errors relative to the BIBC-derived reference. Further tests using third-harmonic data from the modified IEEE 33-bus system characterize PGNN performance under 0–20% noise, reduced training-set sizes, and selected reduced-PMU configurations. These results demonstrate the effectiveness of integrating BIBC-based physical priors with data-driven learning for joint harmonic source localization and nodal harmonic injection-current estimation on the two simulated radial distribution systems considered in this study.