Optimize Grouping and Path of Pylon Inspection in Power System

In order to ensure the normal operation of the power system, it is an essential concern for optimizing inspection path based on limited human and material resources. Despite a wide body of literatures for path planning, however, a framework to optimize grouping and inspection path with minimum number of inspection teams is still lacking. Given the target transmission lines and constrained work hours for each inspector, we study the theoretical solution of the minimum number of inspection teams for task assignment. Furthermore, we develop an improved k-means algorithm, and combine with heuristic intelligent algorithms, such as ant colony algorithm and simulated annealing algorithm, we put forward a universal framework for optimizing grouping and inspection path with minimum number of inspection teams. By applying our framework to both synthetic transmission line and the real transmission lines in Jinhua city, the results verify the theoretical solution of the minimum number of inspection teams. In addition, experimental results demonstrate that our framework can provide quasi-optimal inspection paths and balance work hours for each team. By comparison of the results with different algorithms, we find that the simulated annealing algorithm works the best. Our work paves a new way to solve the vehicle routing problem, travelling salesman problem and some other related problems.