# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 2 Related Work  
In order to analyze the contributions of our work, we first briefly examine the works in relation to SG reconfiguration and fault tolerance. To start with, the reconfiguration of SGs represents a complex discrete optimization problem that can be addressed through various methods, including optimization solvers or metaheuristic algorithms, as well as ML or DL models [12; 13; 14; 15; 16]. The choice of approach typically depends on the complexity of the network and the specific system requirements. In the literature on reconfiguration for SGs, two primary directions have been identified: the first focuses on defining an objective function aimed at optimizing the internal performance of the grid (e.g., minimizing power propagation losses, capacity overloading, and costs), while the second concentrates on reconfiguring the network to enhance resilience and fault tolerance. Delving into specific examples and algorithms, [PERSON] et al. [9] propose a distributed reconfiguration approach for smart grids based on the Open Shortest Path First (OSPF) routing protocol, aiming to enhance fault detection and minimize power losses. This method is implemented in a multi-agent system deployed in secondary substations and validated on the IEEE 123 Node Test Feeder, where agents execute the OSPF algorithm to reconfigure the grid in response to network changes. For Islanded Microgrids (IMGS), [PERSON] et al. [17] introduce a reconfiguration methodology focused on optimizing voltage stability and power flow. Using an Adaptive Multi-Objective Harmony Search Algorithm and validated on IEEE 33-bus and 69-bus systems, this approach improves IMGS performance by increasing loadability and reducing power losses, which is crucial for systems lacking a main grid. Similarly, [PERSON] et al. [18] present a self-healing strategy for microgrid islanding, optimizing generation re-dispatch, network reconfiguration, and load shedding. The problem is modeled as a Mixed-Integer Quadratic Programming (MIQP) to minimize costs and ensure smooth islanding and is validated only on the IEEE 9-bus system. Moreover, for offshore High-Voltage Direct Current (HVDC) transmission systems connected to wind farms, [PERSON] et al. [19] address the challenge of minimizing power losses by proposing a reconfiguration algorithm based on Particle Swarm Optimization (PSO), which optimizes the topology of meshed HVDC grids. However, these works mainly focus on optimizing the internal operating parameters of SGs, typically power losses and network configuration costs, but not potential network failures. Regarding the works focused on providing resilience and fault tolerance in SGs, a growing trend has been observed to place more emphasis on ML and DL. [PERSON] al. [20] present an enhanced K-Nearest Neighbors (KNN) algorithm combined with Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for efficient fault detection and classification, demonstrating robustness and precision suitable for real-time applications in SGs. [PERSON] et al. [21] propose a Convolutional Neural Network (CNN)-based method for fault localization using voltage data, achieving superior accuracy in low-observability scenarios. The approach is validated on IEEE 39-bus and 68-bus systems, showing resilience and effectiveness in complex conditions. Another similar work using CNNs is by [PERSON] et al. [22], who employ Artificial Neural Networks (ANNs) and one-dimensional CNNs (1D-CNNs) for fault management, extracting fault characteristics from raw signals and demonstrating high accuracy in real-time applications on the IEEE 6-bus system. Furthermore, [PERSON] et al. [23] presented a Long Short-Term Memory (LSTM) network for fault diagnosis, targeting the complexities of renewable energy sources. Their method was validated using Simulink SG models in Matlab, and it was found to improve fault prediction accuracy, outperforming traditional techniques and enhancing the reliability of SGs. [PERSON] et al.