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- Title
Data Collecting and Monitoring for Photovoltaic System: A Deep-Q-Learning-Based Unmanned Aerial Vehicle-Assisted Scheme.
- Authors
Zhang, Hao; Liu, Yuanlong; Meng, Jian; Yao, Yushun; Zheng, Hao; Miao, Jiansong; Gu, Rentao
- Abstract
Nowadays, massive photovoltaic power stations are being integrated into grid networks. However, a large number of photovoltaic facilities are located in special areas, which presents difficulties in management. Unmanned Aerial Vehicle (UAV)-assisted data collection will be a prospective solution for photovoltaic systems. In this paper, based on Deep Reinforcement Learning (DRL), we propose a UAV-assisted scheme, which could be used in scenarios without awareness of sensor nodes' (SNs) precise locations and has better universality. The optimized data collection work was formulated as a Markov Decision Process (MDP), and the approximate optimal policy was found by Deep Q-Learning (DQN). The simulation results show efficiency and convergence and demonstrate the effectiveness of the proposed scheme compared with other benchmarks.
- Subjects
PHOTOVOLTAIC power systems; DEEP reinforcement learning; REINFORCEMENT learning; SOLAR power plants; DRONE aircraft
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 21, p11613
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app132111613