An Improved Marine Predators Algorithm-Based UAV Path Planning for 10-kV Distribution Networks Inspection in Live Working Scenarios

Before conducting maintenance on 10-kV distribution networks, the use of unmanned aerial vehicles (UAVs) for inspecting distribution lines can effectively enhance the operational efficiency of personnel in live working scenarios. For UAV-based inspection of power distribution networks, an optimal flight path ensures both operational safety and comprehensive image acquisition in live working scenarios. Therefore, this study proposes a UAV path planning algorithm and an insulator defect classification model based on YOLOv11, aiming to develop a UAV system for live power line detection. Firstly, a UAV path planning model is established to minimize the flight path length and maximize the image acquisition range, which also considers the safety distance constraints between UAVs and live power lines. On this basis, the optimization strategy of the particle swarm optimization (PSO) algorithm is introduced into the marine predictors algorithm (MPA), and a hybrid PSO-MPA algorithm is designed to improve the convergence accuracy of the MPA algorithm and solve the proposed UAV planning model. In addition, an insulator defect detection model has been developed to accurately identify the image information collected by UAVs. In order to improve the accuracy of the YOLOv11 model, the task-separation assignment (TSA) module was introduced into the YOLOv11 model, and a TSA-YOLOv11 model was designed. Experimental results demonstrate that the proposed PSO-MPA algorithm achieves superior convergence accuracy compared to five algorithms, including PSO. When the UAV flight step size is one meter, the PSO-MPA algorithm reduces the objective function value by an average of 49.62% relative to the other algorithms. Additionally, the TSA-YOLOv11 model attained an average accuracy of 96.87% for the insulator defect classification problem.