Reliability Analysis of Monopile Offshore Wind Turbines: Addressing the foundation

Abstract

Offshore wind turbines (OWTs) are essential for capturing renewable energy from wind in marine environments. However, the reliability of OWT systems, especially their monopile foundations, faces various environmental and operational challenges. This dissertation presents a comprehensive reliabiliity analysis of monopile offshore wind turbine systems, focusing on key failure modes such as scour, fatigue, and soil bearing capacity. Advanced probabilistic methods like First Order Reliability Method (FORM) , Second Order Reliability Method(SORM), and Monte Carlo Simulation(MCS) were used to quantify failure probabilities and assess system reliability. Detailed analyses for scour-related failures, fatigue damage, and soil bearing capacity were conducted using empirical data and modeling techniques. The scour analysis revealed a failure probability of 2.2800%, highlighting the importance of accurate scour depth predictions and effective mitigation strategies. The fatigue analysis showed a significant failure probability of 0.5037%, emphasizing the need for robust material selection and rigorous maintenance schedules. The soil bearing capacity analysis indicated a low failure probability of 0.0268%, underscoring the necessity for thorough geotechnical investigations. Fault Tree Analysis(FTA) and Reliability Block Diagram(RBD) were utilized to identify critical failure modes and evaluate overall system reliability. The FTAs for various components—including the foundation, rotor, blades, nacelle, and control system—provided insights into potential failure mechanisms and their probabilities. The RBD for the control system demonstrated an overall reliability of approximately 98%, indicating high system reliability under normal operating conditions. The overall system reliability was about 93%. The findings suggest several strategies to enhance the reliability of OWT systems, such as implementing advanced monitoring systems, optimizing design and material selection, and adopting proactive maintenance practices. Future research directions are proposed, focusing on advanced simulation techniques, material science innovations, and long-term performance data collection.