This research focuses on the application of artificial neural networks (ANNs) on parameters extraction of photovoltaic (PV) models. Extracting parameters of the PV models accurately is crucial to control and optimize PV systems. Although many algorithms have been proposed to address this issue, how to extract the parameters of the PV models accurately and reliably is still a great challenge. Neural network algorithm (NNA) is a recently reported metaheuristic algorithm. NNA is inspired by ANNs. Benefiting from the unique structure of ANNs, NNA shows excellent global search ability. However, NNA faces the challenge of slow convergence rate and local optima stagnation in solving complex optimization problems. This article presents an improved NNA, named neural network algorithm with reinforcement learning (RLNNA), for extracting parameters of the PV models. In RLNNA, three strategies, namely modification factor with reinforcement learning (RL), transfer operator with historical population, and feedback operator, are designed to overcome the challenge of NNA. To verify the performance of RLNNA, it is employed to extract the parameters of the three PV models. Experimental results show that RLNNA can extract the parameters of the considered PV models with higher accuracy and stronger stability compared with NNA and the other 12 powerful algorithms, which fully indicates the effectiveness of the improved strategies.

中文翻译：

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光伏模型参数提取的强化学习神经网络算法


本研究重点关注人工神经网络（ANN）在光伏（PV）模型参数提取中的应用。准确提取光伏模型参数对于控制和优化光伏系统至关重要。尽管已经提出了许多算法来解决这个问题，但如何准确可靠地提取光伏模型的参数仍然是一个巨大的挑战。神经网络算法（NNA）是最近报道的一种元启发式算法。 NNA 的灵感来自于 ANN。受益于ANN独特的结构，NNA表现出优异的全局搜索能力。然而，NNA在解决复杂优化问题时面临着收敛速度慢和局部最优停滞的挑战。本文提出了一种改进的 NNA，称为强化学习神经网络算法（RLNNA），用于提取 PV 模型的参数。在RLNNA中，设计了三种策略，即带有强化学习（RL）的修正因子、带有历史群体的转移算子和反馈算子来克服NNA的挑战。为了验证 RLNNA 的性能，使用它来提取三个 PV 模型的参数。实验结果表明，与NNA及其他12种强大算法相比，RLNNA能够以更高的精度和更强的稳定性提取所考虑的PV模型的参数，这充分说明了改进策略的有效性。