Blar i ODA Open Digital Archive på forfatter "Siddiqui, Muhammad Salman"
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Comparing Deep Learning Based Image Processing Techniques for Unsupervised Anomaly Detection in Offshore Wind Turbines
Keprate, Arvind; Sheikhi, Saeid; Siddiqui, Muhammad Salman (IEEE International Conference on Industrial Engineering and Engineering Management;, Chapter; Peer reviewed; Conference object, 2023)Offshore wind turbines (OWTs) play a crucial role in renewable energy generation, but their remote and harsh environments make them prone to various anomalies that can significantly affect their performance and reliability. ... -
Effect of leading-edge erosion on the performance of offshore horizontal axis wind turbine using BEM method
Mian, Haris Hameed; Siddiqui, Muhammad Salman; Yang, Liang; Keprate, Arvind; Badar, Abdul Waheed (Peer reviewed; Journal article, 2023)This research focuses on the effect of leading-edge erosion on the performance of wind turbines, specifically the GE1.5XLE horizontal axis wind turbine. The blade element momentum (BEM) method is used to predict the ... -
Reliability analysis of 15MW horizontal axis wind turbine rotor blades using fluid-structure interaction simulation and adaptive kriging model
Keprate, Arvind; Bagalkot, Nikhil; Siddiqui, Muhammad Salman; Sen, Subhamoy (Peer reviewed; Journal article, 2023)Over the course of the last four decades, the rotor diameter of Horizontal Axis Wind Turbines (HAWTs) has undergone a substantial increase, expanding from 15 m (30 kW) to an impressive 240 m (15MW), primarily aimed at ...