Solar photovoltaic (PV) characteristic curves (P‐V and I‐V) offer the information required to configure the PV system to operate as near to its optimal performance as possible. Measurement‐based modeling can provide an accurate description for this purpose. This work analyzes the PV module performance and develops a mathematical formula under particular weather conditions to accurately express these curves based on a custom neural network (CNN). The study initially presents several standard mathematical model equations, such as polynomial, exponential, and Gaussian models to fit the PV module measurements. The model selection is subjected to the minimum value of an evaluation parameter. To simplify the solution of the symbolic equations for the CNN network, two neurons in the hidden layer with nonlinear activation function and linear for the output layer were selected. The results show the effectiveness of the proposed CNN model equations over other standard fitting models according to the root mean squared error (RMSE) evaluation. This method promises further improved results with multi‐input parameter modeling.

<span>Harmonic is seen to be an important factor that can influence the performance of different electrical appliances. Earlier studies have regarded the non-sinusoidal power supply as the major source of the harmonic in the induction motors. In this paper, the researcher compared the results generated by a single-phase capacitor induction motor (SIM) using the sinusoidal and non-sinusoidal power supplies. The results in the study could help in comprehensively understanding the impact of the harmonic on the effective performance of the SIM machines when they were powered using a non-sinusoidal power supply. Here, the performance of the SIM when subjected to a maximum load or non-load condition were studied and determined. Furthermore, numerous experimental work was presented to validate the results of the SIM. Power analyses and spectral analysis indicated the actual difference in the overall harmonic wave distortion. It was seen that the current load increased to 89% (i.e., an increase of 11%) when the motor working from the non-sinusoidal source. This investigation highlighted the direct impact of the non-sinusoidal power source on the properties and components of the SIM machine. The vibrations measured under varying conditions and compared the results for the 2 types of power supplies.</span>

Artificial obstructions on the underside of receiver tubes can increase the heat transfer coefficient between the receiver tubes (Heat Exchanger, HX) of Parabolic Trough Collectors (PTCs) and water as a working fluid. In this study, we numerically and experimentally tested the behavior of laminar mixed convective heat transfer in HX tube installed with baffles. These baffles are rings that are connected together axially and connected radially to the inner tube surface. Using ANSYS fluent Version 15.0, we performed computation fluid dynamics modeling to achieve heat transfer enhancement in HX tubes equipped with turbulator baffles under laminar flow conditions. Moreover, the effects of Pitch ratio (P/D) = 3, 6 and 10 and Reynolds number Re#480 were recorded. The novel application of the rings that are connected axially together and radially to the inner tube surface contributes to the long-term storage of thermal energy and promotes heat transfer via. conduction from the tube surface to the center line of the water flow within a short period. In the study, the baffles generated a vortex to increase the Nusselt number (Nu) inside the HX. To simulate heat flux, we calculated the constant wall heat flux of the receiver tube using an electric heater. Results indicated that using 20 rings as baffles instead of plain tubes improves heat transfer by up to 75%. As P/D decreased and re increased, the heat transfer rate, friction factor (f) and Thermal Enhancement Factor (TEF) increased.

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