Photovoltaic (PV) systems have paved their way into general households due to their high efficiency, low deployment cost and huge power savings. These advantages combined with Government incentives further assist in wide-scale adoptability of the solar powered systems. PV systems generate direct current (DC) outputs, which needs to be converted into alternating current (AC) via inverters. The efficiency of inverter design decides the overall efficiency of the PV system, which allows effective utilization of the solar power for feeding to grid or for local usage. In order to design effective inverter models, a large number of electrical configurations are designed by researchers over the years. These include, stand-alone inverters, grid-tie inverters, battery backup inverters and hybrid inverters, each of which are further divided into multiple sub-categories. Each of these sub-categories have a different application, for instance, string-converters are used for moderate power applications up-to 150k W, while central converters are used for high power applications above 80k W, etc. Apart from power capabilities, these designs vary in terms of efficiency of conversion, usability, cost, etc. Due to so many parametric variations, effective selection of these converters for a given PV application becomes ambiguous. In order to reduce this ambiguity, the underlying text statistically evaluates performance of some of the most efficient PV converter models, and compares them on the basis of power capabilities, accuracy of conversion, converter used, control model used, etc. This review will assist researchers and system designers to select the best suited models for their given applications, and thus reduce the time needed for efficient PV inverter system design. This text also recommends future research which can be adopted for improving efficiency of these systems.
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.
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