Topology Identification Method for Distribution Networks Based on Improved T-Type Grey Relational Analysis and Fisher Optimal Segmentation

To address ambiguities in phase-line identification, errors in user–transformer associations, and inaccurate topology records in complex low-voltage distribution networks, this paper proposes a hierarchical topology identification method based on improved T-type grey relational analysis and Fisher optimal segmentation. In the proposed method, signed voltage increments, a resolution coefficient, and node-distance weighting are introduced to construct distance-aware relational features from user voltage sequences. Fisher optimal segmentation is subsequently applied to identify user–transformer associations, followed by a suspicious-user verification mechanism for local topology correction. Case studies using practical transformer-area data show that the proposed method provides greater discrimination among user voltage sequences than Pearson correlation analysis and conventional T-type grey relational analysis. At a random-noise level of 6%, the phase-line identification accuracy remains 83.7%, while the accuracy of user–transformer association identification reaches approximately 93% under the available field-data conditions. Comparative and robustness analyses further indicate that Fisher optimal segmentation maintains relatively stable performance under the tested missing-entry and reduced class-separation conditions. These results suggest that the proposed framework provides a feasible and interpretable approach to topology identification in complex low-voltage distribution networks.