A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels.

Uneven dust accumulation can significantly influence the thermal balance between different regions of photovoltaic (PV) panels, leading to a sharp decrease in power generation efficiency and service life. In this paper, a new identification method for uneven dust accumulation on the surface of PV panels is developed to analyze the dust state (concentration and distribution) quantitatively. First, a novel deep residual neural network (DRNN) is proposed to obtain the regional dust concentration. The residual elements in the model are connected in the form of skipping layers to reduce the order of the weight matrices and improve the network flexibility and the feature learning accuracy. Second, an image preprocessing method is designed to classify the dust accumulation. It includes transformation and correction, removal of the silver grid, nonlinear interpolation, equivalent segmentation, and clustering. A new error evaluation method, error loop, is proposed to analyze the consistency between the measured and the experimental results. The results show that the DRNN has better prediction accuracy than other methods. The R2 and mean absolute error (MAE) of the DRNN are 78.7% and 3.67, respectively. In addition, three conditions are used to verify the performance of the identification method for determining uneven dust accumulation. The average evaluation coefficients of the error loop are 1.19, 0.77, and 1.10, respectively, meeting the design requirements. The proposed method can provide theoretical support for the intelligent operation and maintenance of PV systems. • An identification method for the uneven dust of PV panels is proposed. • A DRNN network architecture of free identity mapping is established. • A multiscale error evaluation method (error loop) is presented. • The model is verified by experiments and error loop evaluation. [ABSTRACT FROM AUTHOR]