Abstract

Modern distribution networks operate under increasingly demanding conditions, characterized by the integration of distributed energy resources, unbalanced three-phase operation, low measurement redundancy, variable topologies, and data uncertainty. In this context, distribution system state estimation (DSSE) is a key tool for operational monitoring; however, its practical deployment is often hindered by topological inconsistencies and gross measurement errors. This paper proposes an integrated and modular deep learning-based methodological framework that combines active topology identification (ATI), gross error detection (GED), error-type identification (ETI), error-location identification (ELI), measurement reconstruction and correction (MRC), and DSSE. The ATI module is formulated as a global multiclass classifier, whereas the subsequent modules are trained as topology-specific models. Compromised measurements are handled through an iterative GED–ETI–ELI–MRC loop that detects, identifies, locates, and corrects one anomalous measurement per iteration before re-evaluating the input vector. The proposed methodology was validated by using simulation-based scenarios generated in OpenDSS for a real unbalanced three-phase 240-node distribution feeder. The results show that no single architecture is dominant across all subproblems: WaveNet1D achieved the best relative performance in ATI, GED, and ETI; EncDec-CNN in ELI; NBEATS1D in MRC; and EncDec-GRU in DSSE. Additionally, WLS estimators based on both nodal voltages and branch currents failed to achieve numerical convergence on the 240-node test system under the evaluated conditions, a finding consistent with recent literature reporting analogous convergence failures in distribution networks of similar or smaller scale. Furthermore, the integrated evaluation shows that omitting ATI increases the voltage-magnitude MAE by a factor of 12.3 and the voltage-angle MAE by a factor of 8.1 with respect to the complete framework, whereas omitting only the compromised-measurement treatment increases these errors by factors of 1.8 and 1.9, respectively. The total offline computational cost was approximately 1587.9 h (66.2 GPU-days), while the online inference latency was approximately 0.45 ms per sample, making the framework compatible with AMI- and SCADA-based monitoring cycles. These findings confirm that topological consistency is the dominant factor in DSSE accuracy and that iterative measurement correction meaningfully improves estimator robustness under anomalous measurement conditions.

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