Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)                   IJEEE 2025, 21(2): 3593-3593 | Back to browse issues page

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FADZAIL N F, Zali S M, Mid E C. Double Sigmoid Activation Function for Fault Detection in Wind Turbine Generator using Artificial Neural Network. IJEEE 2025; 21 (2) :3593-3593
URL: http://ijeee.iust.ac.ir/article-1-3593-en.html
Double Sigmoid Activation Function for Fault Detection in Wind Turbine Generator using Artificial Neural Network
NOOR FAZLIANA FADZAIL , Samila Mat Zali , Ernie Che Mid
Abstract:   (212 Views)
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. 
Keywords: Activation Function, Fault Detection, Artificial Neural Network, Machine Learning, Doubly Fed Induction Generator, Wind Turbine.
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Type of Study: Only For Articles of ELECRiS 2024 | Subject: Artificial Intelligence Techniques
Received: 2024/12/07 | Revised: 2025/03/11 | Accepted: 2025/02/18
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.