Game-theoretic decision-making intelligent learning method for anti-UAV protection of power transmission lines

In response to the task of anti-drone protection for power transmission lines, a game-theoretic decision-making intelligent learning method is proposed targeting the protection device. Firstly, a target-attack-defender (TAD) game model and an optimization mathematical model are constructed based on indicators such as relative distance, time, and energy consumption, which include the power transmission line, invading drones, and the protection device. Then, based on the Proximal Policy Optimization (PPO) deep reinforcement learning (DRL) framework, the game decision learning algorithm is designed to realize the independent intelligent decision of both sides of the game. Simulation experiments demonstrate that the proposed algorithm can effectively enhance the anti-drone protection device’s intelligent decision-making ability, ensuring the safety of power transmission lines.