A Method for Estimating Available Power of Wind Farms by Considering the Power Generation Conditions and Station Losses

doi:10.12096/j.2096-4528.pgt.21135

Abstract

Abstract:

Optimization of direct-regulated wind power automatic generation control (AGC) system and grid online optimal dispatch system requires the establishment of an accurate wind farm available power estimation model. Based on the new energy stand-alone information management system, this paper put forward a method of accurate estimation of wind power plant considering power generation conditions and station losses. The Spearman correlation coefficient was used to determine the number of key historical moments in the estimation moment, and the theoretical power estimation model of wind turbines was established based on the long short-term memory (LSTM) network. The operating conditions of the wind turbines were subdivided into six types (named as waiting wind, power generation and outage etc.), and the equivalent circuit model of the loss in the wind farm was established. Finally, the actual data of a wind farm was used to perform simulation calculations. The calculation results show that the root mean square error is reduced by 40% when the theoretical power model of a single machine considers the historical wind speed;When the available power model of wind farm takes into account the power generation condition and in-station loss, the root square error is reduced by 76.9%. The available power estimation model for wind farms proposed in this paper will facilitate the optimization of online dispatching and direct-regulation wind power AGC system strategies, and improve the level of wind power consumption.

Key words: wind power, Spearman correlation coefficient, long short-term memory (LSTM) network, theoretical power, station loss, available power

CLC Number:

TK 81

Jian YANG, Yu LIU, Kunpeng HUANG, Yazhou LUO, Siqing NIU, Wei WANG, Jiafei HUAN, Lei ZHANG, Pei ZHANG, Huawei LI. A Method for Estimating Available Power of Wind Farms by Considering the Power Generation Conditions and Station Losses[J]. Power Generation Technology, 2023, 44(2): 235-243.

Figures/Tables 9

Fig. 1 Expanded view of LSTM unit in time

Fig. 2 LSTM unit structure

Tab. 1 Correlation coefficient between cabin wind speed at estimated time and cabin wind speed at historical time

估算时刻	历史时刻	相关系数
t时刻	t-5时刻	0.895 7
	t-4时刻	0.912 9
	t-3时刻	0.945 1
	t-2时刻	0.966 8
	t-1时刻	0.975 7

Tab.2 Hyperparameters of wind turbine theoretical generation power estimation model based on LSTM network

超参数	数值
输入层时间步数	5
输入层维数	1
隐藏层的数目	1
隐藏层维数	8
输出变量维数	1

Tab. 3 Errors in the test set of the theoretical power generation estimation model for wind turbines

模型	均方根误差/%	平均绝对误差/%
BP model-1	6.127	5.378
LSTM model-1	5.825	4.304
LSTM model-5	3.071	2.150

Fig. 3 Comparison of partial estimated power values and real power values in test set

Tab. 4 Model errors of wind farm available generation power estimation model

模型	均方根误差/%	平均绝对误差/%
1	18.077	16.663
2	5.708	4.590
3	2.850	1.888
4	2.877	2.511
5	1.320	1.091

Fig. 4 Comparison of estimated value and true value of wind farm available power generation

Fig. 5 Scatter diagram of loss change in the station

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