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

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.

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“…The data on wind speed, direction, and generated power are analyzed to understand the relationship between generated power and available wind power, and then to make predictions. Whether making long-or short-term or ultra-short-term wind power predictions, it is equally important to consider the deterioration of offshore wind turbines, as this significantly affects power output and predicted results (it can be up to 8% deviation from the rated power) [6]. Moreover, considering the degraded conditions of an offshore wind turbine can improve the accuracy of predicted results, thereby reducing the RMSE between the predicted and real-time power output by up to 1% [6].…”

“…Whether making long-or short-term or ultra-short-term wind power predictions, it is equally important to consider the deterioration of offshore wind turbines, as this significantly affects power output and predicted results (it can be up to 8% deviation from the rated power) [6]. Moreover, considering the degraded conditions of an offshore wind turbine can improve the accuracy of predicted results, thereby reducing the RMSE between the predicted and real-time power output by up to 1% [6]. The predicted outcomes help the management team manage power generation on the wind farm, its distribution, and storage at the grid.…”