Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network

doi:10.16183/j.cnki.jsjtu.2023.S1.27

Abstract

Abstract:

The motion prediction of large floating wind turbine platforms is the key technology to realize the control of active ballast systems and intelligent operation and maintenance monitoring. However, the complex environment of floating wind turbines makes ultra-short-term predictions that only rely on physical models and numerical simulation methods very challenging. Therefore, this paper proposes an innovative ultra-short-term prediction method for floating wind turbine platform motion based on the long-short-term memory (LSTM) neural network. Measured data have been used to verify the feasibility and uncertainty of this method in terms of surge motion. The results show that the ultra-short-term prediction method proposed in this paper can obtain a better accuracy. For example, the maximum mean square error of surge motion prediction in the 60 s under working condition is only about 1%. The ultra-short-term motion prediction of large floating wind turbines proposed in this paper provides solid technical support for future intelligent operation and maintenance of floating wind farms.

Key words: large floating wind turbines, ultra-short-term prediction, long-short-term memory (LSTM) network, uncertainty

CLC Number:

TP319
TK 89

WEI Hui, CHEN Peng, ZHANG Ruihan, CHENG Zhengshun. Ultra-Short-Term Platform Motion Prediction Method of Large Floating Wind Turbines Based on LSTM Network[J]. Journal of Shanghai Jiao Tong University, 2023, 57(S1): 37-45.

Figures/Tables 11

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参变量	取值	参变量	取值
窗口长度	20	批量大小	50
网络层数	2	网络节点数	64
最大训练轮数	1000	最小训练轮数	100
初始学习率	0.01	C₀	0.3

编号	时间周期	H_s/m	T_p/s	风速/(m·s^-1)	流速/(m·s^-1)	浪向/(°)	风向/(°)	流向/(°)
1	2018-01-06T08:00	4.4	10.9	13.7	0.21	17	11	19
2	2018-01-09T09:40	3.2	9.3	13.9	0.09	131	146	303
3	2018-01-14T15:40	4.2	8.7	20.4	0.32	165	174	24
4	2018-02-13T01:20	2.1	6.5	15.5	0.27	201	174	187
5	2018-03-26T23:30	2.2	10.6	8.5	0.24	14	172	16
6	2018-04-14T00:40	2.1	10.5	5.2	0.32	107	171	195
7	2018-05-02T04:00	2.3	6.5	15.6	0.12	185	175	233
8	2018-07-29T04:00	3.0	7.9	16.6	0.33	161	179	44
9	2018-02-24T04:50	2.5	7.3	14.1	0.17	164	161	150

预报时长/s	测试集	e_MSE×10²		e_MAPE×10²		R² ×10²		e_ONE×10²
10	工作	4.2±	0.0006	5.59±	0.0043	17.34±	0.1200	3.30±	0.0105
	停机	8.6±	0.0005	14.34±	0.0055	53.20±	0.0266	5.51±	0.0155
30	工作	5.7±	0.0007	6.67±	0.0044	-15.69±	0.1457	4.36±	0.0079
	停机	7.4±	0.0014	12.32±	0.0120	62.73±	0.0849	5.47±	0.0175
60	工作	10.9±	0.0022	9.25±	0.0099	-119.88±	0.4546	7.19±	0.0133
	停机	16.6±	0.0021	19.39±	0.0114	-0.846±	0.1514	10.18±	0.0120