High Impedance Fault Detection and Localization Using Fully-Connected Convolutional Neural Network: A Deep Learning Approach

Abstract

The detection and localization of high impedance faults (HIF) in power systems are challenging due to the low fault current magnitude, which often falls below the detection threshold of conventional devices. HIF events introduce harmonics into the network, posing risks to the safety of connected equipment, including the potential for igniting fire which endangers lives and properties. In this study, Emanuel's HIF model was used to generate HIF signatures resembling real HIF events. Model parameters were adjusted to mimic various contact surface impedances. Two datasets were created: 'no-fault' data, simulating the network without HIF, and 'fault' data, incorporating HIF waveforms by simulating single and multiple lines with the HIF model. The faulted line was divided into five segments along the 33 kV line to capture fault signatures at different locations. The generated data, including current waveforms and magnitudes, were processed and divided into an 80:20 ratio for training, validation, and testing using a deep fully connected Convolutional Neural Network for HIF detection and location. The results showed an impressive accuracy rate of 99.44% and 99.78% for detection and location respectively, representing a significant advancement in HIF detection and location, and offering practical applications for enhancing power line safety.