DDQN-GA: a hybrid algorithm for intelligent inspection path optimisation and process modelling in power systems

As the electricity trading market expands and becomes more complex, ensuring user safety and efficient equipment operation has become a critical challenge for the power industry. Inspection path planning and process modelling, as core technologies in intelligent inspection within the smart manufacturing system, have become essential tools for addressing this challenge. In response to the low efficiency of power system inspections, this paper proposes an intelligent inspection path optimisation and process modelling method (DDQN-GA) based on a combination of double deep Q-Network (DDQN) and genetic algorithm (GA). First, the proposed method employs the DDQN algorithm to intelligently allocate power-trading users and inspection teams, allowing each team to be optimally scheduled based on real-time system status and demand. Subsequently, GA is used to optimise the internal paths of each inspection team, effectively exploring and optimising complex path combinations to minimise overall inspection costs and achieve the optimal inspection plan. Experimental results demonstrate that this method significantly reduces total inspection costs and shortens computation time. Compared with three traditional algorithms, the DDQN-GA approach considerably improves computational efficiency, especially in handling large-scale inspection teams and user allocations.