Treffer: Reliable Estimation of Neutral Current in Industrial Power Systems Using Genetic Algorithm–Based Ensemble Learning and Multimethod Explainability Analysis.

Accurate estimation of neutral current (In) in industrial three‐phase power systems is critical for harmonic suppression, equipment protection, and operational safety. This study proposes an ensemble regression framework optimized by a multiobjective genetic algorithm (GA) using 12,328 real‐field measurements based on 29 electrical characteristics (P, Q, S; Irms; Urms; PF, dPF; ITHD, etc.). The GA simultaneously determines the selection and weights of the base learners (SVR, ANN, GPR, RF, GBR, XGB, DT, and GPR‐RQ), improving eight performance metrics together: RMSE, MAE, SMAPE, MdAPE, R2, EVS, maximum error, and PBIAS. Comparative analyses show that GA achieves high accuracy in 10‐fold cross‐validation compared to PSO, SA, random search, and average voting strategies (e.g., R2 = 0.9972, RMSE = 1.83, and SMAPE = 10.31%); unseen test data maintained competitive overall performance (e.g., R2 = 0.9820; SMAPE = 56.17%). In noise robustness, R2 = 0.9933 was achieved in target‐injected disturbance scenarios. Optimization reached Pareto convergence in approximately 50 generations. In the explainability analysis, SHAP and LIME outputs showed significant differences (p < 0.05) in 28 out of 29 variables; despite low inter‐method correlation (Pearson ≈ −0.022), they provided complementary insights. The results demonstrate that the GA‐XAI–supported ensemble provides high accuracy, interpretability, and applicability for In prediction. To the best of our knowledge, this study presents the first In prediction framework that statistically compares SHAP and LIME when used together with a GA‐optimized ensemble and reports the process in a reproducible MATLAB script. We translate these distinctions into a practical protocol: SHAP for global monitoring and policy and LIME for case‐level triage, thus enabling practitioners to confidently leverage complementary XAI signals during operations. [ABSTRACT FROM AUTHOR]

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