Browsing by Author "Abubakar, Ali"

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    Mathematical Modeling of Transmission Dynamics with Periodic Contact Rate and Control by Different Vaccination Rates of Hepatitis B Infection in Ghana
    (Asian Research Journal of Mathematics, 2021-10-14) Reindorf, Nartey Borkor; Abubakar, Ali; Musah, Anas; Owusu, Frank Kofi; 0000-0002-5721-4638
    The paper evidenced that Hepatitis B infection is the world’s deadliest liver infection and Vaccination is among the principal clinical strategies in fighting it. These have encouraged a lot of researchers to formulate mathematical models to accurately predict the mode of transmission and make deductions for better health decision-making processes. In this paper, an SEIR model is used to model the transmission of the Hepatitis B infection with periodic contact rate and examine the impact of vaccination. The model was validated using estimated data in Ghana and simulated in a MATLAB environment. The results showed that the vaccination rate has a great impact on the transmission mode of the Hepatitis B infection and the periodic contact rate may lead to a chaotic solution which could result in an uncontrolled spreading of the infection. It is concluded that even if the vaccination rate is 70%, the infection rate would reduce to the minimum barest so more newborns must be vaccinated.
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    Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm
    (AJRSP, 2021-08-05) Reindorf, Nartey Borkor; Abubakar, Ali; 0000-0002-5721-4638
    Avoiding over-dependency on the oil-fired energy supply systems motivates many countries to integrate renewable energy into the existing energy supply systems. Solar Photovoltaic technology forms the most promising option for developing such a costeffective and sustainable energy supply system. Generally, the current-voltage curve is used in the performance assessment and analysis of the Photovoltaic module. The accuracy of the equations for the curve depends on accurate cell parameters. However, the extraction of these parameters remains a complex stochastic nonlinear optimization problem. Many studies have been carried out to deal with such problem but still more researches need to be carried out to achieve a minimum error and a high accuracy. The existing researches ignored the variation in the meteorological data though it has a significant impact on the problem design. In this study, the Sample Average Approximation was employed to deal with the uncertainty and the hybrid optimization method was used to get the optimal parameters. The results showed that the Hybrid PSO-GWO produced the most optimal solution: Series resistance(1.4623), Shunt resistance (215.0000), Ideal diode factors (n1 = 0.9500, n2 = 1.6500) with a maximum PV power of 59.850W. The methodology produced realistic results since the variability is dealt with and the Hybrid PSO-GWO finds the optimal solution at a higher convergence rate.
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    Stochastic Optimal Design of Household-Based Hybrid Energy Supply Systems Using Sample Average Approximation
    (Hindawi, 2022-07-07) Reindorf, Nartey Borkor; Abubakar, Ali; Amoako-Yirenkyi, Peter; 0000-0002-5721-4638
    In terms of energy production, combining conventional and renewable energy sources prove to be more sustainable and cost-e ective. Nevertheless, e cient planning and designing of such systems are extremely complex due to the intermittency of renewable sources. Many existing studies fail to capture the stochasticity and/or avoid detailed reliability analysis. is research proposes a practical stochastic multi-objective optimization tool for optimally laying out and sizing the components of a grid-linked system to optimize system power at a low cost. A comparative analysis of four state-of-theart algorithms using the hypervolume measure, execution time, and nonparametric statistical analysis revealed that the nondominated sorting genetic algorithm III (NSGA-III) was more promising, despite its signi cantly longer execution time. According to the NSGA-III calculations, given solar irradiance and energy pro les, the household would need to install a 5.5 (kWh) solar panel tilted at 26.3° and orientated at 0.52° to produce 65.6 (kWh) of power. e best battery size needed to store enough excess power to improve reliability was 2.3 (kWh). e cost for the design was $73520. In comparison, the stochastic technique allows for the construction of a grid-linked system that is far more cost-e ective and reliable.
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    Stochastic Optimal Selection and Analysis of Allowable Photovoltaic Penetration Level for Grid-Connected Systems Using a Hybrid NSGAII-MOPSO and Monte Carlo Method
    (International Journal of Photoenergy, 2023) Abubakar, Ali; Borkor, Reindorf Nartey; Amoako-Yirenkyi, Peter; 0000-0002-5721-4638
    Generally, the main focus of the grid-linked photovoltaic systems is to scale up the photovoltaic penetration level to ensure full electricity consumption coverage. However, due to the stochasticity and nondispatchable nature of its generation, significant adverse impacts such as power overloading, voltage, harmonics, current, and frequency instabilities on the utility grid arise. These impacts vary in severity as a function of the degree of penetration level of the photovoltaic system. Thus, the design problem involves optimizing the two conflicting objectives in the presence of uncertainty without violating the grid’s operational limitations. Nevertheless, existing studies avoid the technical impact and scalarize the conflicting stochastic objectives into a single stochastic objective to lessen the degree of complexity of the problem. This study proposes a stochastic multiobjective methodology to decide on the optimum allowable photovoltaic penetration level for an electricity grid system at an optimum cost without violating the system’s operational constraints. Five cutting-edge multiobjective optimization algorithms were implemented and compared using hypervolume metric, execution time, and nonparametric statistical analysis to obtain a quality solution. The results indicated that a Hybrid NSGAII-MOPSO had better convergence, diversity, and execution time capacity to handle the complex problem. The analysis of the obtained optimal solution shows that a practical design methodology could accurately decide the maximum allowable photovoltaic penetration level to match up the energy demand of any grid-linked system at a minimum cost without collapsing the grid’s operational limitations even under fluctuating weather conditions. Comparatively, the stochastic approach enables the development of a more sustainable and affordable grid-connected system.
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    Stochastic Optimal Selection and Analysis of Allowable Photovoltaic Penetration Level for Grid-Connected Systems Using a Hybrid NSGAII-MOPSO and Monte Carlo Method
    (Hindawi, 2023-03-27) Reindorf, Nartey Borkor; Abubakar, Ali; Amoako-Yirenkyi, Peter; 0000-0002-5721-4638
    Generally, the main focus of the grid-linked photovoltaic systems is to scale up the photovoltaic penetration level to ensure full electricity consumption coverage. However, due to the stochasticity and nondispatchable nature of its generation, significant adverse impacts such as power overloading, voltage, harmonics, current, and frequency instabilities on the utility grid arise. These impacts vary in severity as a function of the degree of penetration level of the photovoltaic system. Thus, the design problem involves optimizing the two conflicting objectives in the presence of uncertainty without violating the grid’s operational limitations. Nevertheless, existing studies avoid the technical impact and scalarize the conflicting stochastic objectives into a single stochastic objective to lessen the degree of complexity of the problem. This study proposes a stochastic multiobjective methodology to decide on the optimum allowable photovoltaic penetration level for an electricity grid system at an optimum cost without violating the system’s operational constraints. Five cutting-edge multiobjective optimization algorithms were implemented and compared using hypervolume metric, execution time, and nonparametric statistical analysis to obtain a quality solution. The results indicated that a Hybrid NSGAII-MOPSO had better convergence, diversity, and execution time capacity to handle the complex problem. The analysis of the obtained optimal solution shows that a practical design methodology could accurately decide the maximum allowable photovoltaic penetration level to match up the energy demand of any grid-linked system at a minimum cost without collapsing the grid’s operational limitations even under fluctuating weather conditions. Comparatively, the stochastic approach enables the development of a more sustainable and affordable grid-connected system.