Quantum mechanics

Engineering

Aerospace engineering

SCADA

Geotechnical engineering

Wind direction

Marine engineering

Wind power forecasting

Power (physics)

Nacelle

Computer science

Wind speed

Physics

Simulation

Submarine pipeline

Geography

Electric power system

Automotive engineering

Environmental science

Artificial intelligence

Turbine

Wind power

Deep learning

Meteorology

Artificial neural network

Offshore wind power

Electrical engineering

Energy

Accurate wind power forecasting is essential for efficient operation and maintenance (O&M) of wind power conversion systems. Offshore wind power predictions are even more challenging due to the multifaceted systems and the harsh environment in which they are operating. In some scenarios, data from Supervisory Control and Data Acquisition (SCADA) systems are used for modern wind turbine power forecasting. In this study, a deep learning neural network was constructed to predict wind power based on a very high-frequency SCADA database with a sampling rate of 1-s. Input features were engineered based on the physical process of offshore wind turbines, while their linear and non-linear correlations were further investigated through Pearson product-moment correlation coefficients and the deep learning algorithm, respectively. Initially, eleven features were used in the predictive model, which are four wind speeds at different heights, three measured pitch angles of each blade, average blade pitch angle, nacelle orientation, yaw error, and ambient temperature. A comparison between different features shown that nacelle orientation, yaw error, and ambient temperature can be reduced in the deep learning model. The simulation results showed that the proposed approach can reduce the computational cost and time in wind power forecasting while retaining high accuracy.

Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network