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This research presents a hybrid approach for state estimation of Doubly Fed Induction Generator (DFIG)-based wind turbines during voltage dips in IoT-enabled systems, utilizing a Multiresolution Sinusoidal Neural Network (MSNN) optimized by the Tasmanian Devil optimization (TDO) algorithm. The proposed MSNN-TDO method achieves a prediction accuracy of 98.8% with a 1.5% error rate, significantly outperforming existing techniques. The study highlights the importance of accurate state estimation for maintainin

Author

marouane

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EN