TY - JOUR
T1 - Wind turbine power modelling and optimization using artificial neural network with wind field experimental data
AU - Sun, Haiying
AU - Qiu, Changyu
AU - Lu, Lin
AU - Gao, Xiaoxia
AU - Chen, Jian
AU - Yang, Hongxing
N1 - Funding Information:
The work presented in this paper is financially supported by the Research Institute for Sustainable Urban Development (No. BBW8) and the University Supporting Fund project (No. BBAV) of The Hong Kong Polytechnic University.
Publisher Copyright:
© 2020 Elsevier Ltd
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12/15
Y1 - 2020/12/15
N2 - The wake effect is a major and complex problem in the wind power industry. Wake steering, such as controlling yaw angles of wind turbines, is a proven approach to mitigate the wake influence and increase the power generation of a wind farm. This paper proposes a power prediction model and optimizes yaw angles to minimize the entire wake impact on wind turbines. The power model adopts the artificial neural network (ANN)with the consideration of the wake effect, so it is called ANN-wake-power model. The model can estimate the total power generation of wind turbines for given wind speeds, wind directions, and yaw angles. A case study has been conducted to introduce the modelling process. The experimental data of five wind turbines from an operating wind farm have been used to train and evaluate the model. The ANN-wake-power model has proven to be effective in estimating the power generation. It performs a good balance between computational cost and accuracy. Subsequently, the model is applied to optimize the yaw angles by using Genetic Algorithm. With the optimized yaw angle strategy, the total power ratio of wind turbines can reach 0.96 in all directions involved. For a row of wind turbines, the optimal yaw control strategy for each wind turbine is different. Finally, it is worth noting that, to achieve a good performance of the ANN-wake-power model, sufficient input data should be adopted in the training process.
AB - The wake effect is a major and complex problem in the wind power industry. Wake steering, such as controlling yaw angles of wind turbines, is a proven approach to mitigate the wake influence and increase the power generation of a wind farm. This paper proposes a power prediction model and optimizes yaw angles to minimize the entire wake impact on wind turbines. The power model adopts the artificial neural network (ANN)with the consideration of the wake effect, so it is called ANN-wake-power model. The model can estimate the total power generation of wind turbines for given wind speeds, wind directions, and yaw angles. A case study has been conducted to introduce the modelling process. The experimental data of five wind turbines from an operating wind farm have been used to train and evaluate the model. The ANN-wake-power model has proven to be effective in estimating the power generation. It performs a good balance between computational cost and accuracy. Subsequently, the model is applied to optimize the yaw angles by using Genetic Algorithm. With the optimized yaw angle strategy, the total power ratio of wind turbines can reach 0.96 in all directions involved. For a row of wind turbines, the optimal yaw control strategy for each wind turbine is different. Finally, it is worth noting that, to achieve a good performance of the ANN-wake-power model, sufficient input data should be adopted in the training process.
KW - Artificial neural network
KW - Wake effect
KW - Wind field experiment
KW - Wind turbine power modelling
KW - Yaw angle optimization
UR - http://www.scopus.com/inward/record.url?scp=85091987622&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2020.115880
DO - 10.1016/j.apenergy.2020.115880
M3 - Journal article
AN - SCOPUS:85091987622
SN - 0306-2619
VL - 280
JO - Applied Energy
JF - Applied Energy
M1 - 115880
ER -