An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things

In this paper a novel embedded system for remote monitoring and fault diagnosis of photovoltaic systems is introduced. The idea is to embed machine leaning algorithms into a low-cost edge device for real-time deployment. First, an artificial neural network is developed to detect faults. Then an effective stacking ensemble learning algorithm is developed to classify the nature of the fault. The method performance is eval-uated through common error metrics such as RMSE, MAE, MAPE, r and confusion matrix. Additional algorithms are also embedded into the edge device in order to remotely control the photovoltaic array parameters. Users can be notified by email and SMS about the state of their photovoltaic array. The Blynk IoT platform is used to monitor remotely the photovoltaic array parameters. The experimental results demonstrate the ability of the proposed embedded system to diagnose and monitor the photovoltaic array with a good accuracy.