Calculation of Transient Stability Limit Based on Convolutional Neural Network

doi:10.11930/j.issn.1004-9649.202301008

Abstract

Abstract:

Current procedures to calculate transient stability limit of interface tie line power transfer, using either time domain simulation method or direct method based on Lyapunov stability theory, are very time-consuming and complex. In view of this problem, a new method to compute transient stability limit of interface power transmission is proposed based on convolutional neural network. Firstly, the system operation data and the experimental simulation data are combined together to formulate the characteristic attributes of the transmission interface. Then certain key features of the transmission interface are selected as the input layer vector of the neural network. And next the nonlinear mapping relationship between the key features of the system and the transient stability limit of interface power transmission is constructed after multiple rounds of training processes. Finally, the reliability and effectiveness of the proposed calculation method are verified by case studies of IEEE14 bus system.

Key words: convolutional neural network, transmission section, transient stability limit, power calculation

Qihe LOU, Rongsheng LI, Jie TAN, Tiejiang YUAN. Calculation of Transient Stability Limit Based on Convolutional Neural Network[J]. Electric Power, 2024, 57(4): 211-219.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202301008

https://www.electricpower.com.cn/EN/Y2024/V57/I4/211

Figures/Tables 12

References 25

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故障数	样本数	训练样本		测试样本
故障数	样本数	数目	占比/%	数目	占比/%
0	100	80	9.41	20	2.35
1	150	120	14.10	30	3.53
2	150	120	14.10	30	3.53
3	100	80	9.41	20	2.35
4	100	80	9.41	20	2.35
5	50	40	4.71	10	1.17
6	50	40	4.71	10	1.17
7	50	40	4.71	10	1.17
8	50	40	4.71	10	1.17
9	50	40	4.71	10	1.17
总计	850	680	80.00	170	20.00

故障数	样本数	训练样本		测试样本
故障数	样本数	数目	占比/%	数目	占比/%
0	100	80	9.41	20	2.35
1	150	120	14.10	30	3.53
2	150	120	14.10	30	3.53
3	100	80	9.41	20	2.35
4	100	80	9.41	20	2.35
5	50	40	4.71	10	1.17
6	50	40	4.71	10	1.17
7	50	40	4.71	10	1.17
8	50	40	4.71	10	1.17
9	50	40	4.71	10	1.17
总计	850	680	80.00	170	20.00

层 (类型)	输出类型	参数
conv1D (Conv1D)	(None, 30, 16)	64
conv1D_1 (Conv1D)	(None, 28, 16)	784
max_pooling1D	(None, 14, 16)	0
conv1D_2 (Conv1D)	(None, 12, 64)	3136
conv1D_3 (Conv1D)	(None, 10, 64)	12352
max_pooling1D_1	(None, 2, 64)	0
flatten (Flatten)	(None, 128)	0
dense (Dense)	(None, 1)	129

层 (类型)	输出类型	参数
conv1D (Conv1D)	(None, 30, 16)	64
conv1D_1 (Conv1D)	(None, 28, 16)	784
max_pooling1D	(None, 14, 16)	0
conv1D_2 (Conv1D)	(None, 12, 64)	3136
conv1D_3 (Conv1D)	(None, 10, 64)	12352
max_pooling1D_1	(None, 2, 64)	0
flatten (Flatten)	(None, 128)	0
dense (Dense)	(None, 1)	129

算法	断面	计算值/MW	误差/%
人工神经元	1	48.90	1.191 7
	2	49.60	1.148 6
	3	7.30	0.894 3
一维卷积	1	49.00	0.573 2
	2	49.60	0.801 7
	3	7.13	0.564 2