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Faculty of Engineering and Built Environment

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Author: search.filters.author.Moyo, SibusisoHas files: Yes

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    Gender and gender mainstreaming In engineering education in Africa
    (Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, 2020) Fomunyam, Kehdinga George; Matola, Noluthando; Moyo, Sibusiso
    In Africa, a lot of debates on the issues of gender gap and gender inequality has raised concerns in engineering education (EE) and engineering workforce. Thus, gender inequality and equity are significant in realizing Sustainable Development Goals (SDGs), and in recent years much has been done to address gender gaps, yet women are still excluded, underrepresented, segregated and relegated inengineering profession and academia. With much sensitization on gender equality, Africa is still far from addressing gender gaps in EE; hence the crux of this paper. This paper was guided by Liberal Feminism theory, focusing on women’s freedom as an autonomy to be free from coercive interference, due to‘gender system’ or patriarchal nature of inherited traditions and institutions. This paper takes a broad look at the concepts of gender and gender mainstreaming in EE in Africa. Specifically, it explores gender and inequality in EE and how gender mainstreaming canbe enacted to address gender gaps in EE, as well as its implications in Africa. Thus, to address these gaps, recommendations such as developing gendersensitive curriculum for EE, adopting policies in facilitating women’s access to training and employment opportunities as well as creating gender-sensitive career counselling were advocated
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    Comparative analysis of different computational intelligence techniques for maximum power point tracking of PV systems
    (University of Oradea, 2022-10-01) Moyo, Ranganai Tawanda; Tabakov, Pavel Y.; Moyo, Sibusiso
    The performance of a photovoltaic (PV) module can be improved by employing maximum power point tracking (MPPT) controllers. MPPT controllers are algorithms that are included in PV battery charge controllers or inverters to extract the maximum available power from PV modules for any given temperature and irradiance. Several studies report that the use of PV modules without MPPT controllers results in power losses, which ultimately results in the need to install more solar panels for the same power requirement. Numerous techniques of varying complexities have been proposed in the literature to solve the MPPT objective function. This paper presents a comparative analysis of three computational intelligence (CI) based MPPT techniques namely, the fuzzy logic (FL) based controller, artificial neural networks (ANN) based controller, adaptive neuro-fuzzy inference system (ANFIS) based controller and one conventional technique, the perturbation and observation (P&O) controller. These MPPT controllers are designed, simulated and analysed in the MATLAB/Simulink environment. The performance of the studied MPPT techniques is evaluated under steady-state weather conditions, rapidly changing weather conditions and varying load conditions. CI-based MPPT controllers are found to be more efficient than the P&O controller. Moreover, the ANFIS-based MPPT controller shows an outstanding MPPT performance for all the scenarios studied.
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    Design and modeling of the ANFIS-based MPPT controller for a solar photovoltaic system
    (ASME International, 2021-08) Moyo, Ranganai T.; Tabakov, Pavel Y.; Moyo, Sibusiso
    Abstract Maximum power point tracking (MPPT) controllers play an important role in improving the efficiency of solar photovoltaic (SPV) modules. These controllers achieve maximum power transfer from PV modules through impedance matching between the PV modules and the load connected. Several MPPT techniques have been proposed for searching the optimal matching between the PV module and load resistance. These techniques vary in complexity, tracking speed, cost, accuracy, sensor, and hardware requirements. This paper presents the design and modeling of the adaptive neuro-fuzzy inference system (ANFIS)-based MPPT controller. The design consists of a PV module, ANFIS reference model, DC–DC boost converter, and the fuzzy logic (FL) power controller for generating the control signal for the converter. The performance of the proposed ANFIS-based MPPT controller is evaluated through simulations in the matlab/simulink environment. The simulation results demonstrated the effectiveness of the proposed technique since the controller can extract the maximum available power for both steady-state and varying weather conditions. Moreover, a comparative study between the proposed ANFIS-based MPPT controller and the commonly used, perturbation and observation (P&O) MPPT technique is presented. The simulation results reveal that the proposed ANFIS-based MPPT controller is more efficient than the P&O method since it shows a better dynamic response with few oscillations about the maximum power point (MPP). In addition, the proposed FL power controller for generating the duty cycle of the DC–DC boost converter also gave satisfying results for MPPT.