Arc Fault Characterization by Using Statistical Parameters

Arc faults pose significant risks in electrical systems, leading to fires, equipment damage and potential loss of life. Therefore, efficient and reliable arc fault detection methods are essential for ensuring electrical safety. This abstract presents a novel approach for arc fault detection based on statistical parameters. The proposed method utilizes statistical techniques to analyze the current waveform in electrical circuits. Through the extraction of statistical features, such as mean, standard deviation, Skewness and kurtosis, the algorithm effectively discriminates between normal operation and arc fault events. The system’s performance is evaluated through extensive simulations and real-world tests, demonstrating its capability to accurately detect and rapidly respond to arc faults in various electrical configurations. The adoption of this methodology promises to enhance the overall safety and reliability of electrical systems, mitigating the potential hazards associated with arc faults.