High-frequency SCAD Data and Deep Learning Neural Network - IT Assignment Help

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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 highaccuracy.Renewable energies are playing an increasingly significant role in reducing global carbon footprint [1]. Among them, wind energy is considered as a great alternative to conventional fossil fuels [2,3]. For instance, European countries have highlighted a marked increase in newly installed offshore wind farms. More specifically, 80% of the world’s newly installed offshore wind was from EU countries at the end of 2017 [1]. Compared with onshore wind farms, offshore wind farms have the advantage of containing plenty of wind sources, lavish construction sites and larger capacity of wind generations [4]. Therefore, the wind turbine industry has seen a continuous move from onshore wind turbines to offshore ones. Meanwhile, due to the uncertain environment that they are locating in and malfunctions of offshore wind turbines, there is an ever-increasing attention on optimizing the performance of offshore wind turbines. The aim is to lower the cost [5,6] and improve the efficiency of energy captured from newly installed renewable energy sources [7]. Accurate power forecasting is a challenging task but essential to wind turbines as they are capable of reducing the operational cost.

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