Synthetic Monitoring Data Generation for Fault Detection in Wind Turbines

The effective detection of faults in wind turbines is crucial to ensure their reliability and reduce downtime. However, the availability of real-world monitoring data representing various fault scenarios is often limited, making it difficult to test and validate fault detection algorithms. This paper presents a method for generating synthetic wind turbine monitoring data using OpenFAST, an open-source simulator developed by the National Renewable Energy Laboratory (NREL). The simulator is used to model the dynamic behavior of a wind turbine under both normal operating conditions and in a specific fault scenario, which is rotor unbalance. By leveraging OpenFAST's ability to simulate the physical response of a wind turbine to environmental conditions and mechanical faults, we can create a comprehensive dataset that mimics real-world monitoring data. This dataset covers various operating conditions, including different wind speeds and directions, enhancing the generalizability of the data for fault detection purposes. The generated data is intended to support the development and testing of fault detection tools, providing a benchmark for algorithms that rely on monitoring data to predict, detect, and diagnose failures in wind turbines. The synthetic dataset aims to fill the gap between theoretical models and real-world applications, facilitating the design of more robust and accurate fault detection methods. This study demonstrates the potential of using high-fidelity simulations for reliability analysis and underscores the value of synthetic data in advancing predictive maintenance strategies for renewable energy systems.

Keywords: Synthetic data generation, Fault detection, Wind turbines monitoring, OpenFAST simulation.