29,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in 6-10 Tagen
payback
15 °P sammeln
  • Broschiertes Buch

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
IR. Dr. Kazi Kutubuddin Sayyad Liyakat ha completado su B.E., M.E., Ph.D. en Ingeniería E&TC y Post Doctorado en "IoT in Healthcare Applications", y actualmente trabaja como Profesor y Jefe de Departamento, Departamento de Ingeniería E&TC y fue Decano R&D. Ha propuesto los enfoques KSK, KSK1, DL, KK y KVS en el campo de la tecnología.