Machine learning-driven renewable energy grid integration stability assessment: LIME interpretability and LLM intelligent analysis

As renewable energy penetration in power grids increases, inherent intermittency and volatility pose severe stability challenges. Traditional assessment methods face computational complexity bottlenecks, while machine learning (ML) models, despite strong predictive performance, suffer from “black-box” opacity limiting adoption in safety-critical systems. This study proposes a comprehensive ML framework integrating LIME interpretability and physical mechanism validation for renewable energy grid stability assessment, using a publicly available synthetic benchmark dataset (2000 samples, 15 features) adhering to IEC/IEEE standards. We systematically compare ten classification models spanning traditional ML (logistic regression, K-nearest neighbors, support vector machine, random forest, gradient boosting) and deep learning (MLP, 1D CNN, LSTM, GRU, tabular ResNet). Results demonstrate traditional methods’ competitive performance on this small-scale tabular dataset—gradient boosting achieves 84.5% accuracy, ROC AUC 0.904, MCC 0.626, outperforming deep architectures constrained by overfitting on 1600 training samples. We emphasize that these findings are specific to the limited dataset scale ($$n=1600$$); deep learning may demonstrate superior performance with substantially larger training sets ($$n > 10,000$$). We develop a four-level LIME interpretability framework: (i) single-sample local interpretation, (ii) global feature importance aggregation identifying FreqDeviation and HarmonicTHD as most critical factors (contributions > 10), (iii) contribution directionality analysis, and (iv) multi-sample consistency verification (CV < 15%). Rationale for CV < 15%: This threshold balances coverage (88% of samples) with reliability, filtering unstable interpretations while retaining robust feature rankings, validated through empirical testing and expert assessment. LIME-identified features align with power system theory—frequency deviation reflects active power imbalance, harmonic distortion affects damping. However, we identify spurious correlations (Sample 127: low harmonics paradoxically promoting instability), underscoring expert validation necessity before deployment. Exploratory LLM integration (GPT-4) demonstrates feasibility of converting LIME outputs into actionable natural language recommendations, achieving expert-rated technical accuracy 4.2 ± 0.6/5. We explicitly acknowledge critical limitations: (i) synthetic data may not capture all real-world complexities (rare failure modes, cascading failures, stochastic weather patterns), requiring validation on actual SCADA/PMU data; (ii) the evaluated deep learning architectures exclude recent state-of-the-art tabular models (e.g., FT-transformer, TabNet), limiting the scope of our deep learning assessment; (iii) model performance may degrade by 5–15% on real data without domain adaptation. This framework advances explainable AI for power systems, providing pragmatic guidance for practitioners and establishing rigorous interpretability methodology transferable to physics-constrained ML tasks.