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

Lin, Z. and Liu, X. (2020) Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network. Energy, 201, 117693. (doi: 10.1016/j.energy.2020.117693)

Abstract

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-second. 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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Dr Xiaolei

Creator Roles

Liu, X.: Conceptualization, Resources, Software, Investigation, Data curation, Writing – review and editing, Supervision

• Authors: Lin, Z., and Liu, X.
• Subjects: T Technology > TA Engineering (General). Civil engineering (General)
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Energy
• Publisher: Elsevier
• ISSN: 0360-5442
• ISSN (Online): 1873-6785
• Published Online: 24 April 2020
• Copyright Holders: Copyright © 2020 Elsevier Ltd.
• First Published: First published in Energy 201: 117693
• Publisher Policy: Reproduced in accordance with the publisher copyright policy