Sakura81537/vestasv52-scada-windturbine-granada
Designed and generated by https://simulatexp.dev Vestas V52 Wind Turbine SCADA Synthetic Dataset - Granada Peri-Urban Installation This synthetic dataset contains comprehensive SCADA (Supervisory Control and Data Acquisition) data simulating a Vestas V52 wind turbine operating in a peri-urban environment in Granada, Spain. The dataset captures 40,000 one-minute aggregated sensor readings across 24 parameters, simulating realistic operational conditions and fault scenarios for… See the full description on the dataset page: https://huggingface.co/datasets/Sakura81537/vestasv52-scada-windturbine-granada.
Designed and generated by https://simulatexp.dev
Vestas V52 Wind Turbine SCADA Synthetic Dataset - Granada Peri-Urban Installation
This synthetic dataset contains comprehensive SCADA (Supervisory Control and Data Acquisition) data simulating a Vestas V52 wind turbine operating in a peri-urban environment in Granada, Spain. The dataset captures 40,000 one-minute aggregated sensor readings across 24 parameters, simulating realistic operational conditions and fault scenarios for predictive maintenance applications.
The dataset encompasses fundamental wind turbine physics and operational dynamics, including aerodynamic power generation governed by the turbine's power curve (cut-in at ~4 m/s, rated power at 12-15 m/s, cut-out at ~25 m/s), active pitch control for power regulation, yaw alignment with wind direction, and thermal management of critical components. The physics calculations incorporate realistic relationships between wind speed, rotor/generator RPM, power output, and blade pitch angles, while accounting for environmental factors such as turbulence intensity typical of peri-urban installations. Temperature dynamics follow thermodynamic principles with thermal inertia effects, and the dataset includes sophisticated modeling of fault progression patterns such as gradual bearing wear, asymmetric heating in generator windings, and aerodynamic imbalances from pitch actuator failures.
The dataset includes four distinct operational scenarios ranging from normal operation and power regulation to various failure modes including early gearbox bearing wear and yaw bearing degradation. Each scenario demonstrates physically consistent sensor signatures with appropriate temporal evolution, making it suitable for developing and validating predictive maintenance algorithms, anomaly detection systems, and condition monitoring applications in wind energy systems.
