Mazen Abdel-Salam’s research while affiliated with Assiut University and other places

In this paper, an improved multiple vector model predictive current control (MPCC) algorithm is proposed, based on the discrete space vector modulation (DSVM) principle. The proposed MPCC algorithm is applied to a permanent magnet synchronous generator in a grid connected wind energy conversion system. The DSVM principle is used to increase the number of available voltage vectors (VVs) of the two-level voltage source converter from 8 VVs to 62 VVs by dividing the sampling-period into four-time segments, which increases control flexibility. The deadbeat principle is used to reduce the computational burden of the proposed MPCC algorithm. The performance of the proposed MPCC algorithm is validated through simulations. The total harmonic distortion of the current injected to the grid on using the proposed MPCC algorithm is 2.74% as compared to 5.96% for the conventional one-time segment MPCC algorithm and 3.64% for the three-time segment MPCC algorithm.

The proposed work is aimed at designing a solar PV water pumping system to serve irrigation requirements of a 50-acre agricultural farm in New Assiut city, and domestic electrical load for nearby buildings to replace the already existing system, which is supplied from the utility grid. Two scenarios are presented for the proposed PV system depending on the type of storage element, whether it is battery bank or combined battery bank and water storage tanks. Modelling and optimal sizing of the components of PV system (PV modules, water tanks, battery bank) is established using Genetic Algorithm (GA) in MATLAB software. The two objective functions for optimization are the cost of energy (COE) and greenhouse gas (GHG) emissions with a constraint on zero loss of power supply probability (LPSP). The optimal design for scenario 1 gives a COE of 0.234 /kWh, total net present cost(TNPC)of 327915, GHG emissions of 7906 kg CO2, and percentage of excess energy of 37.3 %. The optimal design for scenario 2 gives a COE of 0.177 /kWh, TNPC of 247986, GHG emissions of 7277 kg CO2, and percentage of excess energy of 34.2 %. Scenario 2 is found to be more suitable for the present case-study.

Evaporative cooling technique is considered one of the effective methods for improving efficiency and power generation of a photovoltaic (PV) module by reducing the operating temperature of its surface. In this paper a theoretical study of heat transfer through a PV module was conducted to investigate how the calculated cell temperature and module efficiency are influenced by the ambient temperature, solar irradiation, and water flow rate, which affect the heating and cooling rates of the module surface. Experimental investigation was done to confirm the theoretical findings concerning the decrease of cell temperature and hence the increase of module efficiency with the increase of either the air flow on module cooling by using sprinkler for water misting or the mass flow of water on module cooling by using nozzles for making a water film over the module surface. The experimental results show a reduction of 26.94 % in cell temperature on using sprinkler against 28.32 % for nozzles with continuous cooling and 24.14 % using sprinkler against 26.75 % for nozzles with intermittent cooling. Experimental results show that evaporative cooling on using sprinkler and nozzles methods increase the electrical efficiency from 13.04 % without cooling to 14.5 % and 14.75 with continuous cooling against increase of the electrical efficiency to 14.29 and 14.7 with intermittent cooling. The maximum electrical efficiency in the datasheet at standard condition records 15.4 %. This means that the evaporative cooling over the PV module strongly improves the system performance to approach its efficiency at standard test condition STC. There is no significant difference between continuous and intermittent cooling in reducing the cell temperature and thus increasing efficiency. Moreover, intermittent cooling reduces the amount of water used for cooling.

This paper is aimed at proposing a novel method for calculating the resistance-to-ground of three grounding-schemes under known applied-voltage. The schemes include a vertical rod(s), and square/rectangular grids with and without rods. The schemes are buried in a homogenous-soil or two-layer soil with an interface-plane separating the soil layers. The calculation method is based on the current-sphere-simulation-technique (CSST) along with the concept of images. The currents in the vertical-rod and the grid-conductors are simulated by current- spheres of diameters the same as the rod or conductor. The interface-plane between soil-layers is simulated by two sets of equal number of current-spheres. Satisfaction of Dirichlet boundary-condition at the scheme-surface and normal current-density continuity along with the potential-equality boundary-conditions the interface-plane formulates a set of equations, whose solution determines the currents of the simulation-spheres. The sum of sphere-currents simulating the ground-scheme represents the current injected into the surrounding-soil for evaluating the scheme grounding-resistance. The calculated grounding-resistance by the proposed-method agreed with those obtained from COMSOL and CYMGRD with a deviation up to 13.2% for the investigated three grounding-schemes.

The proposed work is aimed at designing a solar PV water pumping system to serve irrigation requirements of a 50-acre agricultural farm in New Assiut city, and domestic electrical load for nearby buildings to replace the already existing system, which is supplied from the utility grid. Two scenarios are presented for the proposed PV system depending on the type of storage element, whether it is battery bank or combined battery bank and water storage tanks. Modelling and optimal sizing of the components of PV system (PV modules, water tanks, battery bank) is established using Genetic Algorithm (GA) in MATLAB software. The two objective functions for optimization are the cost of energy (COE) and greenhouse gas (GHG) emissions with a constraint on zero loss of power supply probability (LPSP). The optimal design for scenario 1 gives a COE of 0.234 /kWh, total net present cost(TNPC)of 327915, GHG emissions of 7906 kg CO2, and percentage of excess energy of 37.3 %. The optimal design for scenario 2 gives a COE of 0.177 /kWh, TNPC of 247986, GHG emissions of 7277 kg CO2, and percentage of excess energy of 34.2 %. Scenario 2 is found to be more suitable for the present case-study.