Wavelet Transform-Based Fault Identification and Classification in Power Systems

Power system faults can lead to significant disruptions, economic losses, and safety hazards. Accurate and timely fault detection and classification are crucial for maintaining system reliability and security. This paper proposes a novel approach utilizing Wavelet Transform (WT) for effective fault detection and classification in power systems. WT, with its ability to provide time-frequency analysis, is well-suited to capture the transient characteristics of fault signals. By decomposing the fault signals into different frequency components, WT enables the extraction of relevant features for fault identification. The proposed method involves applying WT to the post-fault voltage and current signals to obtain wavelet coefficients. These coefficients are then analyzed to identify specific patterns associated with different fault types, such as single-line-to-ground, double-line-to-ground, and three-phase faults. Simulation results demonstrate the effectiveness of the proposed method in accurately detecting and classifying various fault scenarios.