Showing 2 results for Wind Turbine.
G. Hamza, M. Sofiane, H. Benbouhenni, N. Bizon,
Volume 19, Issue 2 (6-2023)
Abstract
In this paper, a wind power system based on a doubly-fed induction generator (DFIG) is modeled and simulated. To guarantee high-performance control of the powers injected into the grid by the wind turbine, five intelligent super-twisting sliding mode controllers (STSMC) are used to eliminate the active power and current ripples of the DFIG. The STSMC controller is a high-order sliding mode controller which offers high robustness compared to the traditional sliding mode controller. In addition, it reduces the phenomenon of chattering due to the discontinuous component of the SMC technique. However, the simplicity, ease of execution, durability, and ease of adjusting response are among the most important features of this control compared to some other types. To increase the robustness and improve the response of STSMC, particle swarm optimization method is used for this purpose, where this algorithm is used for parameter calculation. The simulation results obtained using MATLAB software confirm the characteristics of the designed strategy in reducing chattering and ensuring good power control of the DFIG-based wind power.
Noor Fazliana Fadzail, Samila Mat Zali, Ernie Che Mid,
Volume 21, Issue 2 (6-2025)
Abstract
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.
