Mohd Irwan Yusoff
Thumbnail Image
Preferred name
Mohd Irwan Yusoff
Official Name
Mohd Irwan , Yusoff
Alternative Name
Irwan, Y. M.
Irwan, M. A.M.
Yusoff, Y. M.
Irwan, Mohd
Main Affiliation
Scopus Author ID
57216317186
Researcher ID
GKB-6081-2022

Research Output

Filters

2 1
2
Reset filters

Settings
Now showing 1 - 2 of 2
  • Publication
    Assessment of wind power potential in the North region of Malaysia, Chuping Perlis
    ( 2023-01-01)
    Thiraphorn Bun Leew
    ;
    Leow Wai Zhe
    ;
    Mohd Irwan Yusoff
    ;
    Safwati Ibrahim
    ;
    Tan Xiao Jian
    ;
    Muhammad Irwanto Misrun
    ;
    Kumuthawathe Ananda-Rao
    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.
      7  29
  • Publication
    Assessment of cooling photovoltaic-wind hybrid power controller system for AC load application in tropical climate condition
    ( 2015)
    Mohd Irwan Yusoff
    This research looks at the assessment of cooling photovoltaic (PV)-wind hybrid power controller system for alternating current (AC) load application in tropical climate condition. It has four objectives in order to fulfill the requirement of this research. Firstly, the study of the potential PV and wind power generation in Perlis has been discussed. The data of solar radiation and wind speed were measured at the Centre of Excellence for Renewable Energy (CERE), University Malaysia Perlis in Perlis, Malaysia. The average of solar radiation for the past three years (2011 to 2013) is higher than 3 kWh/m2 which indicates that Perlis is suitable for solar power technology application. Secondly, a new model based on wind direction data in order to estimate the wind speed has been proposed. The development of the theory of circular-linear functional relationship model via circular-linear regression model proposed by Mardia (1976) when both variables are subject to errors are presented. The model has fitted the data quite well by assuming that both variables of the unreplicated circular-linear functional relationship model are subject to errors. This indicates that the proposed method is acceptable and applicable. Third, the temperature of PV module increases when it absorbs solar radiation, causing the decrement of efficiency. Therefore, the proposed topology of PV automatic cooling system is designed, constructed and experimentally researched within this study in order to overcome this challenge. To reduce the PV module surface temperature, direct current (DC) cooling system was designed using three methods which are DC brushless fan, DC water pump and DC hybrid brushless fan with DC water pump. They will make the air movement and water flow circulation at the back side and front side of PV module, respectively. Four temperature sensors were installed on the PV module to detect its surface temperature.
      30  4