Accurate modeling of photovoltaic modules using a 1-D deep residual network based on I-V characteristics
Accurate modeling of photovoltaic modules using a 1-D deep residual network based on I-V characteristics
复制标题
使用基于 I-V 特性的一维深度残差网络对光伏组件进行精确建模
DOI：
10.1016/j.enconman.2019.02.032
复制
发表时间：
2019-04-15
影响因子：
10.4
通讯作者：
You, Linlin
中科院分区：
文献类型：
--
作者：
Chen, Zhicong;Chen, Yixiang;You, Linlin
准确可靠的光伏组件建模对于光伏系统的优化设计、运行和评估具有重要意义。PV模型可以分为基于等效电路的白色盒模型和数据驱动的黑盒模型。由于难以获得地面真实模型参数以及预定模型结构所造成的限制，白盒建模方法对于任意操作条件通常具有相对较低的精度和泛化性能。此外，报告的黑箱模型是基于传统的人工神经网络（ANN），是有效的，但具有有限的性能。在这项研究中，由于快速发展的深度学习技术的高性能，我们提出了一种新的光伏组件黑盒建模方法，使用新的修改的一维深度残差网络（1-D ResNet）和测量的I-V特性曲线，可以在任意操作条件下一次预测整个I-V曲线。为了解决数据不平衡引起的过拟合问题，采用网格采样方法对具有高度非均匀工作条件的原始I-V曲线数据集进行重采样，得到条件相对均匀的数据集，为后续建模提供依据。提出的1-D ResNet为基础的模型进行了全面验证，并提出了一个基于单二极管的白盒模型和其他三个传统的基于人工神经网络的黑盒模型进行了比较，使用大型数据集的测量I-V特性曲线从国家可再生能源实验室（NREL）。实验结果表明，黑盒模型通常优于白盒模型。特别是，所提出的一维ResNet的PV模型是明显上级其他三个传统的人工神经网络的黑箱模型，在精度，泛化性能和可靠性。
Accurate and reliable modeling of photovoltaic (PV) modules is significant for optimal design, operation and evaluation of PV systems. PV models can be classified into equivalent circuit-based white box models and data-driven black box models. Due to the difficulty to obtain the ground true model parameters and the limitation posed by the predetermined model structure, white-box modeling methods generally suffer relatively low accuracy and generalization performance for arbitrary operating conditions. In addition, reported black-box models are based on the conventional artificial neural networks (ANN) that are efficient but have limited performance. In this study, motivated by the high performance of fast developing deep learning techniques, we propose a novel black-box modeling method for the PV modules using a new modified one-dimensional deep residual network (1-D ResNet) and measured I-V characteristic curves, which can predict a whole I-V curve at a time for arbitrary operating conditions. To alleviate the overfitting issue caused by imbalanced data, original I-V curve datasets with highly non-uniform operating conditions are resampled by a grid sampling approach to obtain the datasets with relatively uniform conditions for the subsequent modeling. The proposed 1-D ResNet based model is comprehensively verified and compared with a proposed single-diode based white-box model and three other conventional ANN based black-box models, using large datasets of measured I-V characteristic curves from the National Renewable Energy Laboratory (NREL). Experimental results indicate that black-box models are generally better than the white-box model. Especially, the proposed 1-D ResNet based PV model is obviously superior to other three conventional ANN based black-box models, in terms of accuracy, generalization performance and reliability.