Real-time monitoring of partial shading in large PV plants using Convolutional Neural Network

The performance of a typical string-connected Photovoltaic (PV) panels will be adversely affected even if only one of its panels is partially shaded. Therefore, the monitoring of these panels becomes crucial to ensure reliable operation of the whole PV plant. In this paper, a low-cost solution for real-time monitoring and diagnosis of PV plants is proposed. In fact, our approach is based on the use of pre-trained AlexNet Convolutional Neural Network (CNN) to predict whether a given panel is under partial shading conditions (PSC) or not, by extracting features from two-dimensional (2-D) scalograms generated in real-time from PV data acquisition system. It should be noted that, in addition to a disconnection circuit and a servo motor mounted on it, each panel of the proposed system is equipped with four sensors namely: voltage, current, temperature and irradiance sensor. All these components are connected to Adafruit PyBadge microcontroller unit (MCU). In fact, a Continuous Wavelet Transform was applied to the time series data provided by these sensors to generate the 2-D scalograms. On the hand, we performed transfer learning by fine-tuning the last fully connected layer of the pre-trained AlexNet CNN and modifying its architecture to get only two neurons which represent our two classes (i.e., PV panel under PSC or not). The model, achieving a high fault detection accuracy of 98.05%, was built in Keras framework with TensorFlow where only the parameters of the last fully connected layer of the pre-trained AlexNet CNN are retrained. Afterward, this model was converted into TensorFlow Lite format and successfully implemented on PyBadge MCU. Several experiments were carried out in this work have shown that, our monitoring system allows not only the automatic disconnection of the partial shaded PV panels, to avoid thermal issues under bypass conditions, but also removing undesired objects accumulated on their surface by actioning the corresponding servo motor, in order to restore in real-time the normal operation of the whole PV system.