Climatic changes are one of the biggest problems that humanity faces and renewable energies are a big weapon to fight this threat. Solar energy is one of the renewable energy sources in current use and to produce this type of energy there are several solar plants placed across the country. These giant plants are made of many sets of solar panels (called arrays) which are responsible for converting solar energy into electricity.
One of the critical aspects of these plants' operation is the early detection of solar panel malfunctions. The current methods in use are expensive and consume a lot of time, meaning that, in some cases, the faults are detected only a year later, causing a huge financial impact on the companies responsible for the plants' operation.
To cut these losses and to detect the faults as early as possible, this dissertation presents a real-time system capable of detecting malfunctions in a solar panel array. The node should be placed in the array's junction box and detects if an array has a faulty panel. The faults are detected comparing the array's output (voltage and current) with the output of an artificial neural network that models the array's behaviour using the real-time solar irradiance and temperature values. The neural network uses the measured values to carry out an online learning process, improving the network performance.
Due to the plant's extension, a low power wide area network (LORAWAN), is used to send the array status and the data collected to the cloud, where they are processed and presented in a dashboard
