Design of an efficient MPPT optimization model via accurate shadow detection for solar photovoltaic

Abstract The output of Solar Panels is directly dependent on the intensity of direct Sunlight that is incident on the panels. But this efficiency reduces due to shadow effects for rooftop-mounted panels. These shadows can come from other solar panels, nearby buildings, or high-rise structures. It is possible to optimize Maximum Power Point Tracker (MPPT) controllers, which draw the most power possible from PV modules by forcing them to function at the most efficient voltage to increase the output of solar panels even while they are in the shade. Thus, the MPPT analyses the output of the PV module, compares it to the voltage of the battery, and determines the best power the PV module can provide to charge the battery. It then converts that power to the optimum voltage to allow the battery to receive the maximum level of currents. Additionally, it can power a DC load linked directly to the battery. Existing shadow detection and MPPT control models are highly complex, which increases their computational requirements, thereby reducing the operating efficiency of the solar panels. This text discusses a novel Saliency Map-based low-complexity shadow detection model for Solar panels to overcome this issue. The proposed model initially extracts saliency maps from connected Solar panel configurations and evaluates the background for the presence of shadows. Based on the intensity shadows, the model tunes MPPT parameters for optimal voltage & current outputs. Due to this, the model can maximize Solar panel output by over 8.5%, even under shadows, making it useful for various real-time use cases.