Modeling of Ultra-Short Term Offshore Wind Power Prediction Based on Condition-Assessment of Wind Turbines

Suo Li, Ling-ling Huang, Yang Liu and Meng-yao Zhang
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Suo Li: School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Ling-ling Huang: School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Yang Liu: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Meng-yao Zhang: Economic Research Institute, State Grid Shanghai, Shanghai 200000, China

Energies, 2021, vol. 14, issue 4, 1-16

Abstract: More accurate wind power prediction (WPP) is of great significance for the operation of electrical power systems, as offshore wind power penetration increases continuously. As the offshore wind turbines (OWT) are a key system in converting offshore wind power into electrical power, maintaining their condition plays a pivotal role in WPP. However, it is seldom considered in traditional WPP. This paper proposes an ultra-short term offshore WPP methodology based on the condition assessment (CA) of OWTs. Firstly, a modified fuzzy comprehensive evaluation (MFCE) based CA of the OWT is presented with a new defined deterioration of indicators calculated by the relative errors. Long short-term memory (LSTM) neural network is introduced to deal with the complicated interactions between the various monitoring data of an OWT and the dynamic marine environment. Then, with the classifications of the health conditions of the OWT, the historical operation data is classified accordingly. An OWT-condition based WPP with a backpropagation (BP) neural network is developed to deal with the non-linear mapping relations between the numerical weather prediction (NWP) information, health conditions of OWT, and the output power. The results of the case study show the influences of the OWT health conditions to its output power and verifies the effectiveness and higher accuracy of the proposed method.

Keywords: offshore wind turbines; LSTM; health condition; wind power prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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