# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 7 Conclusions  
In this manuscript, we proposed a novel approach for predicting faults in SGs through the application of ML and DL models to support the reconfiguration process. Building upon the DEN2 DE algorithm, we addressed the complexities of load redistribution in dense and heterogeneous SGs by applying advanced ML and DL techniques to enhance decision-making during the grid's reconfiguration phase. By utilizing real-world datasets such as SustDataED and integrating simulated photovoltaic energy production data from tools like PVWatts, this work demonstrated the feasibility of employing AI-driven models to predict potential grid faults and improve system resilience.  
The proposed methodology effectively identified optimal routes for load balancing while minimizing errors in energy distribution. Among the evaluated models, the Random Forest classifier, optimized with RFECV as a dimensionality reduction technique, consistently demonstrated superior performance. This configuration achieved high levels of Precision (94.28%) and Recall (81.05%), ensuring a balanced error detection capability with a reduced false alarm rate, as compared to other models. Extensive testing and validation in simulated environments confirmed that this RF-RFECV configuration enhanced the robustness and adaptability of the SG, managing the uncertainties associated with renewable energy sources and dynamic changes within the grid topology effectively.  
Future research could build on the current findings by investigating additional ML and DL models to further enhance fault prediction capabilities. Additionally, extending the application of these models by simulating additional time samples to gain further insights, or to a broader range of datasets beyond SustDataED, potentially encompassing different regions, scales, and topological configurations, even different reconfiguration algorithms, would contribute to improving the generalization and robustness of the proposed approach. An important direction for future work also includes evaluating the developed models in light of the intrinsic characteristics of smart grids, such as distributed generation, fluctuating load demands, and the integration of renewable energy sources, to better understand their adaptability and performance in real-world smart-grid environments.  
**Author Contributions:** Conceptualization, D.C. and P.B.; methodology, D.C.; software, P.B.; validation, D.C. and P.B.; formal analysis, D.C., P.B., and D.L.-P.; investigation, D.C., P.B., and E.R.; resources, E.R.; data curation, D.C. and P.B.; writing--original draft preparation, D.C. and P.B.; writing--review and editing, E.R., D.L.-P., N.M., and J.D.-F.; visualization, D.L.-P., N.M., and J.D.-F.; supervision, D.C. and E.R.; project administration, E.R. and D.L.-P.; funding acquisition, E.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by project TED2021-131301B-I00 funded by MICIU/ AEI/10.13039/501100011033, and by a grant from Universidad de Alcala through \"Contratos Predoctorales de Formacion de Personal Investigador--FPI-UAH 2022\".  
**Data Availability Statement:** The original data presented in the study are openly available in KeyMonData at [[https://github.com/[PERSON]/TFM](https://github.com/[PERSON]/TFM)]([https://github.com/[PERSON]/TFM](https://github.com/[PERSON]/TFM)) (accessed on 14 October 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.