Haitham A. Mahmoud

Incorporating bypass diodes within photovoltaic arrays serves to mitigate the negative effects of partial shading scenarios. These situations can lead to the appearance of multiple peaks in the performance of solar panels. Nevertheless, there are cases where conventional maximum power point tracking (MPPT) techniques could encounter inaccuracies, causing them to identify the highest power point within a specific area (the local maximum power point; LMPP) instead of the overall highest power point across the entire array (the global maximum power point; GMPP). Numerous methods based on artificial intelligence (AI) were proposed to address this issue; however, they frequently used cumbersome and unreliable methodologies. This research presents the energy-valley-optimizer-based optimization (EVO) technique, which is designed to efficiently and dependably tackle the issue of partial shading (PS) in detecting the maximum power point (MPP) for photovoltaic (PV) systems. The EVO algorithm enhances the speed of tracking and minimizes power output fluctuations during the tracking phase. Through the utilization of the Typhoon hardware-in-the-loop (HIL) 402 emulator, extensive validation of the proposed technique is conducted. The effectiveness of the suggested method is compared with the established cuckoo search algorithm for achieving maximum power point tracking (MPPT) within a photovoltaic (PV) system. This comparison takes place under equivalent conditions to ensure a fair performance evaluation.

The design and development of a photovoltaic thermal (PVT) collector were developed in this study, and electrical and electrical thermal efficiency were assessed. To improve system performance, two types of coolants were employed, liquid and liquid-based MnO nanofluid. Flow rates ranging from 1 to 4 liters per minute (LPM) for the interval of 1.0 LPM were employed, together with a 0.1% concentration of manganese oxide (MnO) nanofluid. Various parametric investigations, including electrical power generation, glazing surface temperature, electrical efficiency, and electrical thermal efficiency, were carried out on testing days, which were clear and sunny. Outdoor studies for the aforementioned nanofluids and liquids were carried out at volume flow rates ranging from 1 to 4 LPM, which can be compared for reference to a freestanding PV system. The research of two efficiency levels, electrical and electrical thermal, revealed that MnO water nanofluid provides better photovoltaic energy conversion than water nanofluid and stand-alone PV systems. In this study, three different domains were examined: stand-alone PV, liquid-based PVT collector, and liquid-based MnO nanofluids. The stand-alone PV system achieved a lower performance, the liquid-based MnO performed better, and the liquid-based PVT achieved an intermediate level.