Stepwise Inertial Intelligent Control of Wind Power for Frequency Regulation Based on Stacked Denoising Autoencoder and Deep Neural Network

doi:10.16183/j.cnki.jsjtu.2022.157

Abstract

Abstract:

Stepwise inertial control (SIC) provides a step-increase of power after load fluctuation, which can effectively prevent system frequency decline and ensure the safety of grid frequency. However, in the power recovery stage, secondary frequency drop (SFD) is easy to occur. Therefore, it is necessary to optimize SIC to obtain a better frequency regulation effect. The traditional method has the disadvantages of high calculation dimension and long consuming time, which is difficult to meet the requirements of providing the optimal control effect in different scenarios. In order to realize the optimal stepwise inertial fast control of wind power frequency regulation in load disturbance events, this paper introduces the deep learning algorithm and proposes a stepwise inertial intelligent control of wind power for frequency regulation based on stacked denoising autoencoder(SDAE) and deep neural network(DNN). First, sparrow search algorithm (SSA) is used to obtain the optimal parameters, and SDAE is used to extract the data features efficiently. Then, DNN is used to learn the data features, and the accelerated adaptive moment estimation is introduced to optimize the network parameters to improve the global optimal parameters of the network. Finally, the stepwise inertial online control of wind power frequency regulation after disturbance event is realized according to SDAE-DNN. The simulation analysis is conducted for a single wind turbine and a wind farm in the IEEE 30-bus test system. Compared with the results obtained by the traditional method, shallow BP neural network and original DNN network, it is found that the proposed network structure has a better prediction accuracy and generalization ability, and the proposed method can achieve a great effect of stepwise inertia frequency regulation.

Key words: stepwise inertial control (SIC), secondary frequency drop (SFD), sparrow search algorithm (SSA), stacked denoising autoencoder (SDAE), deep neural network (DNN)

CLC Number:

TM732

WANG Yalun, ZHOU Tao, CHEN Zhong, WANG Yi, QUAN Hao. Stepwise Inertial Intelligent Control of Wind Power for Frequency Regulation Based on Stacked Denoising Autoencoder and Deep Neural Network[J]. Journal of Shanghai Jiao Tong University, 2023, 57(11): 1477-1491.

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预测网络	层数	算法	输入/输出层神经元个数	隐藏层神经元个数
SDAE-DNN	3+4	ADAM/ NADAM	3, 3/3, 2	9, 27, 18/ 6, 12, 6, 4
DNN	4	ADAM	3, 2	6, 12, 6, 4
BP	1	SGD	3, 2	12

预测网络	t/s	e_MSE
预测网络	t/s	ΔP_up	ΔT_up
SDAE-DNN	0.238	0.0025	0.0019
DNN	0.187	0.0081	0.0063
BP	0.153	0.0415	0.0347

预测网络	t/s	e_MSE
预测网络	t/s	ΔP_up	ΔT_up
SDAE-DNN	0.179	0.0093	0.0079
DNN	0.143	0.0171	0.0134

v_w/s	风电占比/%	负荷扰动量(p.u.)	ΔP_up (p.u.)	ΔT_up/s	f_nadir/Hz	最大RoCoF/(Hz·s^-1)
4	5	0.01	0.0026	8.32	49.9676	0.0258
4	5	0.03	0.0076	8.40	49.9027	0.0707
4	5	0.05	0.0127	8.41	49.8382	0.1079
5	10	0.05	0.0124	8.09	49.8346	0.1143
5	30	0.05	0.0130	7.29	49.8285	0.1449
5	50	0.05	0.0137	6.41	49.8208	0.1982
6	30	0.05	0.0126	7.22	49.8267	0.1458
8	30	0.05	0.0119	7.09	49.8234	0.1473
10	30	0.05	0.0112	7.02	49.8201	0.1489

参数	基于SDAE和DNN的SIIC	基于FTT的SIC	虚拟惯性控制	下垂控制	风电不参与调频
f_nadir/Hz	49.8989	49.8905	49.8768	49.8739	49.8611
最大RoCoF/(Hz·s^-1)	0.0860	0.0885	0.1037	0.1036	0.1041