A Fault Identification Method for Animal Electric Shocks Considering Unstable Contact Situations in Low-Voltage Distribution Grids

The struggling and twitching of an electrically shocked body in low-voltage distribution grids can complicate and diversify the electric shock current, posing challenges to traditional electric shock detection methods. To tackle this issue, we propose a fault identification method for animal electric shocks considering unstable contact situations. First, a low-voltage distribution network grounding fault experimental platform is established in a real system, creating a sample library that encompasses various operational scenarios. Second, based on the dynamic characteristics of contact resistance and the principles of arc breakdown generation, an electrical equivalent modeling analysis is conducted on the unstable contact region. The underlying mechanisms that increase the complexity and diversity of electric shock current are revealed. Then, innovative features such as “the sum of periodic point differences,” “energy fluctuation arrays,” “pulse regularity,” and “the count of reversals in the direction of changes” are proposed. By integrating several traditional features and automatically extracted encoded features, a feature vector for animal electric shock faults is established from the perspectives of dynamic amplitude, high-frequency components, energy, and encoded features. In addition, a light gradient boosting machine optimized by multiattraction mechanism zebra optimization has been developed. This model achieves accuracy in pattern recognition tasks through its unique ensemble computation and one-sided sampling mechanism, while also reducing time and space costs. Finally, the proposed method is compared with three existing electric shock identification methods using our experimental platform's testing set. The results indicate that the proposed method demonstrates superiority in terms of accuracy for identifying animal electric shocks.