Shi Shuqi, Liu Zongze, Deng Xiaofei, Chen Sifan, Song Dongran
Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang University, Shaoyang 422000, China.
School of Automation, Central South University, Changsha 410083, China.
Sensors (Basel). 2023 Nov 24;23(23):9379. doi: 10.3390/s23239379.
Conventional wind speed sensors face difficulties in measuring wind speeds at multiple points, and related research on predicting rotor effective wind speed (REWS) is lacking. The utilization of a lidar device allows accurate REWS prediction, enabling advanced control technologies for wind turbines. With the lidar measurements, a data-driven prediction framework based on empirical mode decomposition (EMD) and gated recurrent unit (GRU) is proposed to predict the REWS. Thereby, the time series of lidar measurements are separated by the EMD, and the intrinsic mode functions (IMF) are obtained. The IMF sequences are categorized into high-, medium-, and low-frequency and residual groups, pass through the delay processing, and are respectively used to train four GRU networks. On this basis, the outputs of the four GRU networks are lumped via weighting factors that are optimized by an equilibrium optimizer (EO), obtaining the predicted REWS. Taking advantages of the measurement information and mechanism modeling knowledge, three EMD-GRU prediction schemes with different input combinations are presented. Finally, the proposed prediction schemes are verified and compared by detailed simulations on the BLADED model with four-beam lidar. The experimental results indicate that compared to the mechanism model, the mean absolute error corresponding to the EMD-GRU model is reduced by 49.18%, 53.43%, 52.10%, 65.95%, 48.18%, and 60.33% under six datasets, respectively. The proposed method could provide accurate REWS prediction in advanced prediction control for wind turbines.
传统风速传感器在多点测量风速时面临困难，且缺乏对预测转子有效风速（REWS）的相关研究。激光雷达设备的使用能够实现准确的REWS预测，从而为风力涡轮机启用先进控制技术。基于激光雷达测量数据，提出了一种基于经验模态分解（EMD）和门控循环单元（GRU）的数据驱动预测框架来预测REWS。通过EMD将激光雷达测量的时间序列进行分解，得到本征模态函数（IMF）。将IMF序列分为高频、中频、低频和残差组，经过延迟处理后分别用于训练四个GRU网络。在此基础上，通过平衡优化器（EO）优化的加权因子对四个GRU网络的输出进行汇总，得到预测的REWS。利用测量信息和机理建模知识，提出了三种具有不同输入组合的EMD-GRU预测方案。最后，通过在具有四波束激光雷达的BLADED模型上进行详细仿真，对所提出的预测方案进行了验证和比较。实验结果表明，与机理模型相比，在六个数据集下，EMD-GRU模型对应的平均绝对误差分别降低了49.18%、53.43%、52.10%、65.95%、48.18%和60.33%。所提出的方法可为风力涡轮机的先进预测控制提供准确的REWS预测。
