Adaptive AI-Based Fault Detection in Smart Grids: A Data-Driven Approach

Smart grids require intelligent and adaptive mechanisms to ensure reliability and stability, particularly in managing unexpected faults under dynamic operating conditions. Traditional fault detection approaches often struggle with uncertainty, non-linear behavior, and the complexity of large-scale grid environments, limiting their effectiveness in real-world applications. To address these limitations, this study introduces the Neuro-Fuzzy Hybrid Diagnostic Framework (NFHDF), which integrates data-driven anomaly detection with fuzzy logic to provide robust and adaptive fault classification even under uncertain and fluctuating grid scenarios. The proposed framework enhances decision-making accuracy by leveraging neural-based learning for pattern recognition and fuzzy reasoning for handling ambiguous fault conditions. Applied to smart grid environments, NFHDF improves real-time fault detection, minimizes false alarms, and accelerates response times. Experimental results demonstrate that the method significantly enhances detection accuracy, adaptability, and reliability, thereby supporting resilient and efficient grid operations.