Theoretical Enhancement of Energy Production Performance in PV Arrays through Effective Shadow Detection Using Hybrid Technique

Abstract

This manuscript proposes an effective hybrid approach for shadow detection to increase the energy production of solar photovoltaic (PV) arrays. The proposed method is the joint operation of a deep neural network (DNN), and a gradient boosting decision tree (GBDT), together called as DNN-GBDT. The key aim is to improve the energy production performance of photovoltaic arrays. The proposed method dynamically reconfigures the PV system by tracking the shadow. The proposed method contains the following: (1) a camera system constantly monitors the photovoltaic array for moving the shadow track, (2) the image processing unit identifies the shading parts as full or partial, (3) the control decision unit finds the optimum panel layout. Then, by merging the original image and the shadow prior map, the deep neural network (DNN) efficiently trains the shadow samples. The output received is given to the GDBT classification and is processed by the identified and the background image to detect the foreground region. By this process, the normal region and the shadow region are detected. The performance of the proposed method is implemented in MATLAB platform and is compared with various existing methods. The maximum power of PV is 490 W, which is higher than the existing methods.