Path planning for transmission line unmanned aircraft inspection based on forest fire risk

With the establishment of high-voltage transmission lines across forested areas, their inspection becomes crucial to reduce the fire risk of transmission lines and forest areas. At present, few studies have studied the path planning for unmanned aircraft inspection of transmission lines based on the fire risk in forest areas, but they do not address the security of the operation and maintenance of the power grid system or consider the interactions between different influencing factors. Therefore, an unmanned aerial vehicle path planning framework for forest power grid inspection is proposed based on the analytic network process method and genetic algorithm. Moreover, a path optimization method based on the maximum deflection angle constraint is developed.

After determining the assessment routes, the framework integrates field research and historical data to determine the objective data of these routes and identifies six classes of factors affecting the risk of forest fires: combustible factors, terrain factors, meteorological factors, human factors, surface wet conditions, and rescue conditions. These factors are subdivided into 18 typical factors by researching the historical accident cases and related literature. The forest fire risk indicator system is developed using typical factors, and to guarantee that this indicator system can effectively reflect the actual risk level, herein, the typical factors selected are those that are commonly used and recognized by previous researchers. Subsequently, weights for these typical factors are computed based on the analytic network process. Compared with the hierarchical analysis method, which is traditionally applied in earlier works, the network analysis method has the advantage of considering the interactions between the factors. The weights with objective data are combined to calculate the fire risk value for each grid. The high fire risk grids are employed as inspection nodes, and the shortest inspection path is acquired using path planning via the unmanned aerial vehicle inspection based on the genetic algorithm to reach the objective of obtaining real-time data in a short time, at low cost, and with high coverage. For the nodes in the path that do not meet the maximum deflection angle constraint, path optimization is conducted by adding new optimization nodes and the shortest path is ensured under the condition that the roadbed meets the maximum deflection angle constraint.

Sections #3542—#3547 of the line of an important transmission channel in Anhui are taken as an application object. Ten high fire risk areas around the line are determined, and path planning is performed on them. The proposed framework yields an optimal path length of 5 391.72 m, and the path length optimized based on the maximum deflection angle is 5 401.36 m. Here, the path length is only increased by 0.179% compared with the original one. This indicates that the path optimization method not only makes the original path satisfy the constraint of maximum deflection angle, but also increases the path length to be shorter, which has good optimization effect.

This work presents a path planning framework for the unmanned aerial vehicle inspection based on the results of fire risk assessment considering the interactions between various forest fire risk factors. In addition, the proposed path optimization method can make the path satisfy all constraints with a small increase in the path length. The proposed framework and optimization method offer reference and future ideas for realizing the unmanned aerial vehicle inspection of transmission lines in forest areas.