Fault location in distribution networks based on an Improved Black-winged Kite Algorithm

With the large-scale integration of distributed generations (DGs) and the expansion of distribution networks, the complexity of fault location has significantly increased. Traditional fault location methods struggle to meet the demands of modern power systems. This paper explores the impact of distributed generation access on fault location in distribution networks and constructs switching functions and evaluation functions suitable for multi-state distributed generation based on fault characteristic information from automated monitoring devices. To augment the optimization performance of fault localization algorithms, this paper proposes an Improved Black Kite Algorithm (IBKA) that incorporates an Elite Learning Strategy along with Lens Imaging Reverse Learning Strategy and Golden Sine Strategy. This enhances the ability to escape local optima effectively. Simulation results demonstrate that the IBKA exhibits high localization accuracy and fault tolerance in complex scenarios, including single-section faults, multiple-section faults, and Feeder Terminal Unit (FTU) information distortion in active distribution networks, significantly improvs convergence speed and solution efficiency.