Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm

Li, Tenghui, Liu, Xiaolei, Lin, Zi and Morrison, Rory (2022) Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm. Energy, 239 (Part D). p. 122340. ISSN 0360-5442

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Official URL: https://doi.org/10.1016/j.energy.2021.122340

Abstract

Offshore wind energy is drawing increased attention for the decarbonization of electricity generation. Due to the unpredictable and complex nature of offshore aero-hydro dynamics, the Wind Turbine Power Curve (WTPC) model is an important tool for power forecasting and, hence, providing a reliable, predictable, and stable power supply. With the development of data-driven approaches, the Artificial Neural Network (ANN) has become a popular method for estimating WTPCs. This paper integrates the Isolation Forest (iForest), Nonsymmetric Fuzzy Means (NSFM) Radial Basis Neural Network (RBFNN), and metaheuristic algorithm to form a novel WTPC model. iForest performed anomaly detection and removal, NSFM RBFNN approximated the WTPC, and the metaheuristic solved NSFM optimization without training RBFNN. Four real-world datasets were used to assess the performance of NSFM RBFNN. According to multiple evaluation metrics and the Diebold-Mariano test, the accuracy of NSFM RBFNN was significantly better than the other competitive neural network-based methods. Additionally, NSFM RBFNN was shown to be more robust to anomalies than competitors, which is highly beneficial for practical applications.

Item Type: Article
Additional Information: Funding Information: The authors would like to thank Ørsted for providing the SCADA data. This research was also partly funded by the EPSRC Doctoral Training Partnership (EP/R513222/1 ENG).
Uncontrolled Keywords: Offshore wind power, Wind turbine power curve (WTPC), Radial basis function neural network (RBFNN)
Subjects: H600 Electronic and Electrical Engineering
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 25 Nov 2021 11:53
Last Modified: 25 Nov 2021 12:00
URI: http://nrl.northumbria.ac.uk/id/eprint/47845

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