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Iranian Journal of Electrical and Electronic Engineering، جلد ۲۱، شماره ۲، صفحات ۳۵۹۳-۳۵۹۳
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عنوان انگلیسی Double Sigmoid Activation Function for Fault Detection in Wind Turbine Generator using Artificial Neural Network
چکیده انگلیسی مقاله 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. 
کلیدواژه‌های انگلیسی مقاله Activation Function, Fault Detection, Artificial Neural Network, Machine Learning, Doubly Fed Induction Generator, Wind Turbine.
نویسندگان مقاله | NOOR FAZLIANA FADZAIL
Faculty of Electrical Engineering & Technology, University Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia.


| Samila Mat Zali
Faculty of Electrical Engineering & Technology, University Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia.


| Ernie Che Mid
Faculty of Electrical Engineering & Technology, University Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia.
نشانی اینترنتی http://ijeee.iust.ac.ir/browse.php?a_code=A-10-5462-1&slc_lang=en&sid=1
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زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده Artificial Intelligence Techniques
نوع مقاله منتشر شده Only For Articles of ELECRiS 2024
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