Shading type and severity diagnosis in photovoltaic systems via I-V curve imaging and two-stream deep neural network

Abstract

Shading is one of the most common anomalies in photovoltaic (PV) systems, leading to power loss and hotspot phenomenon. Currently, most works can only realize shading detection but cannot further diagnose the type and severity of shading. This paper proposes an effective method for diagnosing shading types combining I-V curve imaging with two-stream deep neural networks (DNN), and estimating severity of five common types of shading in actual operating PV systems. In this method, the I-V curves of PV strings are first resampled and converted to standard test conditions (STC) for eliminating the effects of data scale and environmental factors on shading diagnosis results. Then, a time series imaging method called Gramian angular summation field (GASF) is used to enhance the features of shading. Additionally, a two-stream DNN combining long short-term memory (LSTM) and improved two-dimensional convolutional neural network (2D-CNN) is developed to integrate the characteristic information of I-V curves and 2D images. Furthermore, combining the PV mechanism models and characteristics of I-V curves, this work further estimates the severity of different types of shading in operating PV systems considering the effects of aging loss. The effectiveness and generalization of the proposed method are validated via simulated and experimental data obtained from simulation model and an actual PV platform.