Safwati Ibrahim
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Preferred name
Safwati Ibrahim
Official Name
Safwati, Ibrahim
Alternative Name
Ibrahim, Safwati
Safwati, Ibrahim
Safwati, I.
Ibrahim, S.
Main Affiliation
Scopus Author ID
57191950629
Researcher ID
GDL-1857-2022

Research Output

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  • Publication
    Development A Portable Solar Energy Measurement System
    ( 2021-07-26)
    Atika Z.
    ;
    Leow Wai Zhe
    ;
    Iszaidy I.
    ;
    Mohd Irwan Yusoff
    ;
    Safwati Ibrahim
    ;
    Muhammad Irwanto Misrun
    ;
    Wafi N.M.
    ;
    Saw S.X.
    This project presents the design and development a portable measurement device for measure and monitor solar panel parameters by using Internet of Things (IoT) concept. Solar energy measurement plays a very important role in the measurement of parameter reading for the determination of output generated, but the challenge is only performed manually at the work site using a clamp meter or a multimeter. Furthermore, it was very difficult to get the value at that time, and the data recovery error occurred. There are three specific objectives have been used for the project. Firstly, the relevant circuits for this project are design and built the circuit by using software. The output of the measurement solar irradiance, ambient temperature, solar panel temperature, current and voltage value were displayed on LCD. Next, IoT concept is used for solar panel measurement and monitoring. The value of the measurement and monitoring is used ThingSpeak cloud and ThingView application on the smartphone. It can be collected the portable solar for the energy measurement system can monitor on site, anywhere and anytime using IoT platform.
  • Publication
    Assessment of wind power potential in the North region of Malaysia, Chuping Perlis
    ( 2023-01-01)
    Thiraphorn B.L.
    ;
    Leow Wai Zhe
    ;
    Safwati Ibrahim
    ;
    Irwan Y.M.
    ;
    Muhammad Irwanto Misrun
    ;
    Tan X.J.
    ;
    Ananda-Rao K.
    The wind turbines is a main device that convert the kinetic energy from blades to electrical energy. Before installing wind turbines, the Weibull probability distribution must be calculated to determine the certain wind speed probability. Many problems will come if there no analysis the characteristics of wind in selected location, such as wind speed that not suitable for building wind farm to supply the population in that area. Shape and scale factors, which be controlled in a variety of ways, influence the Weibull distribution. Many studies have looked into which of the various Weibull parameter estimation methods is the most dependable. However, because the results of these investigations were inconsistent, research into more trustworthy Weibull parameter estimation methods is still ongoing. An analysis of data collected Chuping, Perlis for two years was conducted in this study (from 2018 to 2019). By using statistical analysis to evaluate the Weibull distribution method, this study used three methods to compared the Weibull parameters and identified the most reliable and effective method to obtain the Weibull probability distribution by using a three approach that compares the variances of RMSE, MSE and R2, which provides comprehensive insight into level error and volatility. Modified maximum likelihood method, graphical method, and power density method are the three methods used in this study. Therefore, the graphical method has the best accuracy in the wind speed distribution prediction, several methods such as the modified maximum likelihood method, and the power density method have the worst prediction of the wind speed distribution based on all the statistical method variances for this region.
  • Publication
    Simulation study on photovoltaic panel temperature under different solar radiation using computational fluid dynamic method
    ( 2020-01-07)
    Leow W.Z.
    ;
    Mohd Irwan Yusoff
    ;
    Safwati Ibrahim
    ;
    Muhammad Irwanto Misrun
    ;
    Amelia A.R.
    ;
    Syafiqah Z.
    ;
    Muhammad Izuan Fahmi Romli
    ;
    Norfadilah Rosle
    The electrical production is the primary performance of any solar photovoltaic (PV) system. The PV panel operating temperature is inversely proportional to the electrical production of the PV panel. The operating temperature of PV panel is influenced by solar radiation absorbed and the ambient temperature. In the present work, Computational Fluid Dynamics (CFD) method is used to investigate a three-dimensional (3-D) model of a PV panel. It is also essential to estimate the thermal behaviour of the PV panel under various environmental conditions. The primary purpose of this current work is to analyse temperature distribution from the PV panel under given operating conditions. The model geometry is built by using CATIA design software. ANSYS software was simulated the different intensity of solar radiation that applied to the PV panel in order to observe the temperature distribution on each layers of the PV panel. The ambient temperature of the simulation is fixed 35C according to the maximum ambient temperature captured in Malaysia. The simulation results show that an increase in solar radiation intensity along with the PV panel operating temperature increase.
  • Publication
    Comparative Study of Three Methods for Determining Weibull Parameters in Pauh Putra, Perlis
    ( 2021-06-11)
    Thiraphorn B.L.
    ;
    Leow Wai Zhe
    ;
    Mohd Irwan Yusoff
    ;
    Safwati Ibrahim
    ;
    Muhammad Irwanto Misrun
    ;
    Kumuthawathe Ananda-Rao
    ;
    Amelia A.R.
    This paper studied about analysis characteristics of wind speed at Pauh Putra, Perlis, where nearest to Chuping station, Perlis, Malaysia. The wind speed characteristics consist of monthly and annual wind speed in Perlis, Malaysia. By using Weibull distribution, three different methods to calculate the potential of wind power generation and analysis the characteristics of wind speed at Pauh Putra, Perlis. The results present the means wind speed is 1.0790 m/s and 1.1321 m/s for 2018 and 2019, respectively. The highest monthly mean wind speed occurred in February for both years, 2018 and 2019. Besides, the lowest monthly wind speed for 2018 in May and for 2019 in October. The Weibull distribution summarized the highest probability density is 120% in the wind speed, 1.1 m/s using the Maximum Likelihood Method (MLM) method for these two years. Furthermore, this research found that the Energy Pattern Factor (EPF) Method is stretched to the right, and its height decreased from other methods for both years based on the graph of the wind speed of probability density function. The Maximum Likelihood Method (MLM) for these two years is higher because its shape parameters are relatively higher based on the graph of the wind speed of probability density function.