Noor Fazliana Fadzail
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Preferred name
Noor Fazliana Fadzail
Official Name
Noor Fazliana, Fadzail
Alternative Name
Fadzail, Noor Fazliana Bt
Fadzail, N. F.
Fadzail, Noor Fazliana
Binti Fadzail, Noor Fazliana
Main Affiliation
Scopus Author ID
57193318102
Researcher ID
CPB-1523-2022

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  • Publication
    Stator winding fault detection of induction generator based wind turbine using ANN
    (Institute of Advanced Engineering and Science (IAES), 2020-07)
    Noor Fazliana Fadzail
    ;
    Samila Mat Zali
    ;
    M. A. Khairudin
    ;
    Noor Hasnizam Hanafi
    This paper presents a stator winding faults detection in induction generator based wind turbines by using artificial neural network (ANN). Stator winding faults of induction generators are the most common fault found in wind turbines. This fault may lead to wind turbine failure. Therefore, fault detection in induction generator based wind turbines is vital to increase the reliability of wind turbines. In this project, the mathematical model of induction generator based wind turbine was developed in MATLAB Simulink. The value of impedance in the induction generators was changed to simulate the inter-turn short circuit and open circuit faults. The simulated responses of the induction generators were used as inputs in the ANN model for fault detection procedures. A set of data was taken under different conditions, i.e. normal condition, inter-turn short circuit and open circuit faults as inputs for the ANN model. The target outputs of the ANN model were set as ‘0’ or ‘1’, based on the fault conditions. Results obtained showed that the ANN model can detect different types of faults based on the output values of the ANN model. In conclusion, the stator winding faults detection procedure for induction generator based wind turbines by using ANN was successfully developed.
  • 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.
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