Machine learning based signal coverage optimization technology for power distribution and utilization scenarios

The distribution scenario is an important part of the power system, and its wireless network design is important for improving the signal quality of power terminals, realizing comprehensive monitoring and management, enhancing the efficiency of the power system, supporting intelligent decision-making and control, and promoting energy management and energy conservation. In this paper, we propose a machine learning-based distribution network scenario coverage optimization technique for typical scenarios of distribution networks. Firstly, a distribution typical scenario environment model is established based on actual power business terminals and demands. Secondly, ray-tracing technique is used to generate channel big data and build a generalized regression neural network (GRNN) based pathloss prediction model. Finally, the total coverage, average single node coverage, and repeated coverage are combined as reward values and reinforcement learning is used to optimize node deployment locations. The simulation results show that the proposed GRNN pathloss prediction model can accurately predict the pathloss, and the proposed method of calculating the reward function by combining multi-dimensional indicators can effectively improve the convergence rate of the network. The proposed algorithm is important for the planning and design of wireless networks in distribution networks.