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In this study, an ANN was used to predict the high-frequency components of the system and to develop a control strategy to mitigate their effects. The results of the study showed that the ANN was able to accurately predict the high-frequency components of the system and that the control strategy was able to effectively mitigate their effects. This study demonstrates the potential of ANNs for mitigating the high-frequency components in a modern distribution system. This work presents a novel approach to mitigating high-frequency components in a modern distribution system using an Artificial…mehr

Produktbeschreibung
In this study, an ANN was used to predict the high-frequency components of the system and to develop a control strategy to mitigate their effects. The results of the study showed that the ANN was able to accurately predict the high-frequency components of the system and that the control strategy was able to effectively mitigate their effects. This study demonstrates the potential of ANNs for mitigating the high-frequency components in a modern distribution system. This work presents a novel approach to mitigating high-frequency components in a modern distribution system using an Artificial Neural Network (ANN). The proposed method utilizes the capability of an ANN to learn the complex relationship between system parameters and high-frequency voltage harmonics. The trained ANN model is then used to predict the high-frequency components and generate control signals to mitigate them.
Autorenporträt
Dr. Kazi Kutubuddin Sayyad Liyakat has completed his B.E., M.E., and Ph.D. in E&TC Engineering and is nowadays working as a Professor & Head of Department, E&TC Engineering Department. He is Post-Doctoral Fellow working on IoT in Healthcare Applications. His area of Interest is IoT, IoRT, IoBT, AI, ML, and AIIoT.