ABSTRACT
Over the past few years, the use of DC-DC buck-boost converters for Photovoltaic (PV) in renewable energy applications has increased for better results. One of the main issues with this type of converter is that output voltage is achieved with the undesired ripples. Many models are available in the literature to address this issue, but very limited work is available that achieves the desired goal using deep learning-based models. Whenever it comes to the PV, then it is further limited. Here, a deep learning-based model is proposed to reduce the steady-state time and achieve the desired buck- or boost mode for PV modules. The deep learning-based model is trained using data collected from the conventional PID controller. The output voltage of the experimental setup is 12V while the input voltage from the PV modules is 10V (when the sunlight decreases) to 24V (for 3.6 kVA) to 48V (for more than 5 kVA). It is among the few models using a single big battery (12V) for off-grid and on-grid for a single building. Experimental results are validated using objective measures. The proposed model outperforms the conventional PID controller-based buck-boost converters. The results clearly show improved performance in terms of steady-state and less overshoot.
ABSTRACT
In the past decade, solar photovoltaic (PV) modules have emerged as promising energy sources worldwide. The only limitation associated with PV modules is the efficiency with which they can generate electricity. The dust is the prime ingredient whose accumulation on the surface of PV impacts negatively over its efficiency at a greater rate. This research aims to explore the effects of dust accumulation on the energy output and operating temperature of polycrystalline silicon PV panels situated in two different climatic regions of Pakistan, i.e., Islamabad and Bahawalpur. In both the regions, one PV module is kept in ambient environment for six weeks to allow dust to deposit over its surface and perform experimental analysis with one clean module as reference for performance comparison. After six weeks of atmospheric exposure, dusty modules displayed significantly smaller efficiency as a function of different dust densities in the two regions. Dust samples from both cities are collected and analyzed to evaluate their structural attributes and composition. The PV module in Islamabad region had a dust layer over its surface with a density of 6.388 g/m2 and its efficiency was reduced by 15.08%. In Bahawalpur region, the dust density was observed to be 10.254 g/m2 which caused the output power to be slashed by 25.42%. Temperature analysis of modules shows that dust increases their temperatures which is also a quantity responsible for lower PV power generation with same amount of irradiance. The research findings are crucial for determining and predicting PV power degradation in two different atmospheres and determining the schedule of cleaning cycle.
