Automatic Electrical System Fault Diagnosis Using a Fuzzy Inference System and Wavelet Transform

Abstract

Electrical systems consist of varied components that are used for power distribution, supply, and transfer. During transmission, component failures occur as a result of signal interruptions and peak utilization. Therefore, fault diagnosis should be performed to prevent fluctuations in the power distribution. This article proposes a fluctuation-reducing fault diagnosis method (FRFDM) for use in power distribution networks. The designed method employs fuzzy linear inferences to identify fluctuations in electrical signals that occur due to peak load demand and signal interruptions. The fuzzy process identifies the fluctuations in electrical signals that occur during distribution intervals. The linear relationship between two peak wavelets throughout the intervals are verified across successive distribution phases. In this paper, non-recurrent validation for these fluctuations is considered based on the limits found between the power drop and failure. This modification is used for preventing surge-based faults due to external signals. The inference process hinders the distribution of new devices and re-assigns them based on availability and the peak load experienced. Therefore, the device from which the inference outputs are taken is non-linear, and the frequently employed wavelet transforms are recommended for replacement or diagnosis. This method improves the fault detection process and ensures minimal distribution failures.