Mohd Rizal Manan
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Preferred name
Mohd Rizal Manan
Official Name
Mohd Rizal , Manan
Alternative Name
Manan, M. R.
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Scopus Author ID
36514937100
Researcher ID
FKS-3641-2022

Research Output

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  • Publication
    Fusion wind and solar generation forecasting via neural network
    ( 2021-08-27)
    Mahmoud Mustafa Yaseen Mohammed Al Asbahi
    ;
    Muhammad Naufal Mansor
    ;
    Mohd Rizal Manan
    ;
    Mohd Azri Abd Aziz
    ;
    Roejhan Md Kawi
    ;
    Farah Hanan Mohd Faudzi
    Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore. power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction proposed in this paperwork to support a small microgrids system. The system is a combination of hardware of solar panel, wind turbine, hybrid charge controller, current sensor, voltage sensor circuit, battery, Arduino Mega and personal computer that is install with MATLAB along with artificial neural network model for load forecast. The prediction model is known as Feedforward back propagation (FFBP) artificial neural network (ANN), this method utilizes a learning relationship between wind-solar power output and predicted weather. The FFBP model trained ANN to recognize similar pattern and to predict the output power based on train and tested data and the results achieved 99.5 accuracy, 6.25% MAPE and 1.2 % MAD.
      2  37
  • Publication
    Implementation of Two-Stage Multilevel Inverter System using PIC Controller
    (Penerbit Akademia Baru, 2022-10-01)
    Mohd Saifizi Saidon
    ;
    Muhammad Izuan Fahmi Romli
    ;
    Muzaidi Othman @ Marzuki
    ;
    Wan Azani Wan Mustafa
    ;
    Muhammad Zaid Aihsan
    ;
    Mohd Rizal Manan
    ;
    Mohd Azri Abd Aziz
    In this study, the Cascaded H-Bridge Multilevel Inverter (CMLI) is described for use with infrared dryer loads. Because of the low harmonic distortion content and reduced voltage stress in the switching devices, CMLI is one of particular interest. The CMLI topology and the Selective Harmonic Elimination Pulsed Width Modulation (SHEPWM) technology were studied and evaluated. To evaluate the inverter, SHEPWM modulation was studied and applied. The system also includes a DC-DC converter. The converter was designed to be used in infrared drying system powered by direct current (DC) power where an increased output voltage is required. This study also presented an evaluation of performance using infrared load of the CMLI based on power used at 100 W. As a result, a comparison of input power was made, and an assessment into the converter's power quality in terms of harmonic content and overall efficiency was conducted. The implementation of the system by hardware had been able to reduce the harmonic to 15.5%.
      5  35
  • Publication
    Fusion wind and solar generation prototype design with Neural Network
    ( 2021-08-27)
    Mahmoud Mustafa Yaseen Mohammed Al Asbahi
    ;
    Muhammad Naufal Mansor
    ;
    Mohd Rizal Manan
    ;
    Mohd Azri Abd Aziz
    ;
    Roejhan Md Kawi
    ;
    Farah Hanan Mohd Faudzi
    Wind and solar power are the most common renewable resources of energy and their usage for power generation is quickly growing all over the world. However, both wind and solar power are difficult to predict manually due to every time changes in weather condition; therefore, power output of wind and solar is associated with some uncertainty. A reliable wind-solar day ahead load prediction with neural network was proposed to support a small microgrids system. All the system performance measurement such as sensitivity, specificity and accuracy give higher than 90%.
      4  38