基于卷积神经网络的暂稳极限功率计算

doi:10.11930/j.issn.1004-9649.202301008

摘要/Abstract

摘要：

目前求取联络线暂态稳定传输功率极限的时域仿真法和基于李雅普诺夫稳定性理论的直接法计算过程复杂，针对此问题，提出基于卷积神经网络的输电断面暂态稳定极限功率计算方法。首先将系统运行的数据与实验仿真数据相结合，转化为输电断面特征属性，选择输电断面关键特征，将其作为神经网络的输入层向量，然后经过多次训练构建出系统关键特征与输电断面暂态稳定极限功率的非线性映射关系。最后以IEEE14节点进行算例分析，验证了计算方法的可靠性以及有效性。

关键词: 卷积神经网络, 输电断面, 暂态稳定极限, 功率计算

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

娄奇鹤, 李荣盛, 谭捷, 袁铁江. 基于卷积神经网络的暂稳极限功率计算[J]. 中国电力, 2024, 57(4): 211-219.

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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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202301008

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

图/表 12

图 1 输电断面暂态稳定极限传输功率计算流程

Fig.1 Flow chart of transient stability limit calculation of interface power transmission

图 2 网络瞬时等值模型

Fig.2 Network instantaneous equivalence model diagram

图 3 一维时序CNN结构图

Fig.3 CNN structure of one-dimensional time series

图 4 CNN的暂态稳定极限的功率计算流程

Fig.4 Power transfer limit calculation process of convolutional neural network

图 5 IEEE14节点系统结构

Fig.5 IEEE 14-bus system

表 1 数据集样本组成

Table 1 Dataset Composition

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

图 6 四层卷积模型结构图

Fig.6 Four-layer convolution model structure

表 2 卷积网络参数

Table 2 Convolution network parameters

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

图 7 不同卷积核模型学习曲线

Fig.7 Learning curves of different convolution kernel models

图 8 训练轮数与精确度关系图

Fig.8 Relationship between training rounds and accuracy

表 3 断面暂稳极限计算结果

Table 3 Calculation results of interface transient stability limit

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

表 4 IEEE14节点系统暂稳极限功率计算对比

Table 4 Comparison of transient stability calculation for IEEE 14-bus system

方法	故障种类	时间/s	准确度/%
时域仿真法	单一故障	150.6	96.2
时域仿真法	复杂故障	356.0	94.6
人工神经元	单一故障	123.5	89.1
人工神经元	复杂故障	153.2	80.3
一维卷积	单一故障	141.2	96.3
一维卷积	复杂故障	308.9	95.1

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