Integrated Signal Processing and Machine Learning Model for Accurate Power System Fault Detection

Accurate and timely fault detection in power systems is essential to ensure system reliability, minimize equipment damage, and maintain continuous power delivery. This paper proposes an integrated signal processing and machine learning (ML) model designed to enhance the precision and speed of power system fault diagnosis. The approach leverages advanced signal processing techniques—such as Wavelet Transform and Fast Fourier Transform (FFT)—to extract critical time–frequency features from voltage and current signals under various fault conditions. These extracted features are then fed into optimized machine learning classifiers, including Support Vector Machines (SVM), Random Forest (RF), and Deep Neural Networks (DNN), to accurately identify fault types, locations, and severities. The integration of signal processing with ML significantly improves fault detection accuracy compared to conventional methods, particularly under noisy and dynamic operating conditions. Simulation results on standard IEEE test systems demonstrate the model’s robustness and scalability, achieving high accuracy and reduced computational latency. The proposed hybrid framework provides a reliable diagnostic tool for real-time monitoring and intelligent decision-making in modern power grids. Future work will focus on extending the model for predictive maintenance and integration with smart grid communication infrastructures.