基于数据驱动和概率神经网络的光伏阵列智能故障诊断与监控OA

To improve the diagnostic accuracy of current photovoltaic array fault diagnosis methods,this paper proposes an in-telligent fault diagnosis and monitoring method for photovoltaic arrays based on data-driven and probabilistic neural networks.A photovoltaic array model is constructed.The normal and faulty operation data of the photovoltaic array model are integrated into 9 types of fault samples as input data.A fault diagnosis algorithm model is designed by the particle swarm optimizer and probabilistic neural networks based on the ideals of parallel and transfer learning.The experimental results confirm that the fault diagnosis accuracy rate of the proposed algorithm reaches 97.81％,with an improvement range of 1.51％to 11.20％compared to various deep learning algorithms.It still maintains a robustness of over 95％under different degrees of noise inter-ference.