Smart line planning method for power transmission based on D3QN-PER algorithm

Abstract

The planning of power transmission line projects encompasses vast and complex geographical terrains. To address the complexity of transmission line planning and achieve lower line costs, this study proposes a novel intelligent line planning method. For the first time, it combines the Dueling Double Deep Q Network (D3QN) with the prioritized experience replay (PER) mechanism. First, correlate the reward function with metrics such as line length, number of corner points, and geographical environmental data, which are pertinent to the construction costs of power transmission line. Second, the D3QN algorithm is formulated by integrating Double DQN and Dueling DQN. The network's input information is divided into two components during training, aligning with the characteristics of power transmission line planning projects. Finally, the convergence efficiency of the algorithm is improved by using the PER mechanism for the problem of cost difference due to the different number of corner points in the planning path. In order to test the feasibility of the algorithm, we conducted experiments using real maps. Compared with the traditional ant colony optimization (ACO) algorithm, the D3QN-PER deep reinforcement learning algorithm reduces the line length by more than 4% and the number of corner points by more than 60%.