Ernie Che Mid
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Preferred name
Ernie Che Mid
Official Name
Ernie, Che Mid
Alternative Name
Che Mid, Ernie Binti
Che Mid, Ernie
Mid, E. C.
Scopus Author ID
25655179600
Researcher ID
V-7047-2019

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  • Publication
    Investigation of MPC performance for wind turbine system during wind speed uncertainty
    ( 2022-01-01)
    Nurul Afiqah Nabilah Zainudin
    ;
    Surina Mat Suboh
    ;
    Mohd Zamri Hasan
    ;
    Ernie Che Mid
    ;
    Ahmad N.B.
    ;
    Nor Hanisah Baharudin
    ;
    Shamsul Bahar Yaakob
    Real-time implementation for wind turbines (WTs) needs a controller that could explicitly formulate the system constraints and uncertainty in the design process to avoid undesired behavior or breakdown. Model-Predictive-Control (MPC) approach will be used in this research due to its ability to cover actuator and state constraints as well as multivariable control in a more convenient way. To investigate the impact of ad-hoc constraints and wind speed uncertainties, the MPC controller will first be developed. This paper will observe the effect on wind turbines (WT) during uncertainty happen. Multiple uncertainties are simulated to investigate the behavior of the wind turbine system. The simulation results using MATLAB Simulink output are expected to indicate that the MPC controller can ensure the system stability to meet the desired output while satisfying all of the constraints. During the presence of uncertainty, it shows that the MPC controller takes time to stabilize the system.
      1  35
  • Publication
    Double sigmoid activation function for fault detection in wind turbine generator using artificial neural network
    (Iran University of Science and Technology, 2025-06)
    Noor Fazliana Fadzail
    ;
    Samila Mat Zali
    ;
    Ernie Che Mid
    The activation function has gained popularity in the research community since it is the most crucial component of the artificial neural network (ANN) algorithm. However, the existing activation function is unable to accurately capture the value of several parameters that are affected by the fault, especially in wind turbines (WT). Therefore, a new activation function is suggested in this paper, which is called the double sigmoid activation function to capture the value of certain parameters that are affected by the fault. The fault detection in WT with a doubly fed induction generator (DFIG) is the basis for the ANN algorithm model that is presented in this study. The ANN model was developed in different activation functions, namely linear and double sigmoid activation functions to evaluate the effectiveness of the proposed activation function. The findings indicate that the model with a double sigmoid activation function has greater accuracy than the model with a linear activation function. Moreover, the double sigmoid activation function provides an accuracy of more than 82% in the ANN algorithm. In conclusion, the simulated response demonstrates that the proposed double sigmoid activation function in the ANN model can effectively be applied in fault detection for DFIG based WT model.
      2
  • Publication
    Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network
    (Institute of Advanced Engineering and Science (IAES), 2024)
    Noor Fazliana Fadzail
    ;
    Samila Mat Zali
    ;
    Ernie Che Mid
    The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.