Zhichao Zhang, Qihang Liu, Chuyuan Wang, and Yujie Zong
Photovoltaic power generation system, efficiency optimisation, enhance learning, adaptive control, model predictive control (MPC)
Photovoltaic power generation is an important component of achieving sustainable development of renewable energy, and improving the efficiency of photovoltaic power generation is crucial. This paper proposes an efficiency optimisation method for photovoltaic power generation systems based on reinforcement learning and adaptive model predictive control (MPC). The method combines reinforcement learning algorithms with MPC to optimise the control parameters through reinforcement learning algorithms, achieving dynamic adaptive control of photovoltaic power generation systems. Firstly, the reinforcement learning algorithms and interactive learning optimal control strategies are adopted in order to increase adaptability and robustness in different environmental conditions. Secondly, the rolling optimisation of predictive control is achieved to increase efficiency and stability in photovoltaic power generation systems. In addition, the adaptive control mechanism dynamically adjusts control parameters by monitoring environmental parameters and system status in real- time, ensuring that the system maintains optimal performance under various operating conditions. Finally, experimental results demonstrate that the proposed optimisation algorithm not only significantly increases accuracy and control efficiency of a system but also significantly boosts stability and reliability in complex environments for greater application potential.
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