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.

Add to citation manager EndNote|Ris|BibTeX

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

1	周强, 汪宁渤, 冉亮, 等. 中国新能源弃风弃光原因分析及前景探究[J]. 中国电力, 2016, 49 (9): 7- 12, 159.
	ZHOU Qiang, WANG Ningbo, RAN Liang, et al. Cause analysis on wind and photovoltaic energy curtailment and prospect research in China[J]. Electric Power, 2016, 49 (9): 7- 12, 159.
2	李岳. 暂态稳定约束的极限传输能力的快速求取方法研究[D]. 北京: 华北电力大学, 2011.
	LI Yue. Research on the fast calculating method of transient stability constrained power transfer limits[D]. Beijing: North China Electric Power University, 2011.
3	和敬涵, 罗国敏, 程梦晓, 等. 新一代人工智能在电力系统故障分析及定位中的研究综述[J]. 中国电机工程学报, 2020, 40 (17): 5506- 5516.
	HE Jinghan, LUO Guomin, CHENG Mengxiao, et al. A research review on application of artificial intelligence in power system fault analysis and location[J]. Proceedings of the CSEE, 2020, 40 (17): 5506- 5516.
4	李永康, 刘宝柱, 胡俊杰. 基于数据驱动与时域仿真融合的电力系统暂态稳定快速评估[J]. 电网技术, 2023, 47 (11): 4386- 4396.
	LI Yongkang, LIU Baozhu, HU Junjie. Rapid evaluation of power system transient stability based on fusion of data-driven and time-domain simulation[J]. Power System Technology, 2023, 47 (11): 4386- 4396.
5	胡伟, 郑乐, 闵勇, 等. 基于深度学习的电力系统故障后暂态稳定评估研究[J]. 电网技术, 2017, 41 (10): 3140- 3146.
	HU Wei, ZHENG Le, MIN Yong, et al. Research on power system transient stability assessment based on deep learning of big data technique[J]. Power System Technology, 2017, 41 (10): 3140- 3146.
6	王国政, 郭剑波, 马士聪, 等. 电力系统增强智能分析初探[J]. 中国电机工程学报, 2020, 40 (16): 5079- 5088.
	WANG Guozheng, GUO Jianbo, MA Shicong, et al. Preliminary study of power system enhanced intelligence analysis[J]. Proceedings of the CSEE, 2020, 40 (16): 5079- 5088.
7	汤奕, 崔晗, 李峰, 等. 人工智能在电力系统暂态问题中的应用综述[J]. 中国电机工程学报, 2019, 39 (1): 2- 13, 315.
	TANG Yi, CUI Han, LI Feng, et al. Review on artificial intelligence in power system transient stability analysis[J]. Proceedings of the CSEE, 2019, 39 (1): 2- 13, 315.
8	田鹏飞, 于游, 董明, 等. 基于CNN-SVM的高压输电线路故障识别方法[J]. 电力系统保护与控制, 2022, 50 (13): 119- 125.
	TIAN Pengfei, YU You, DONG Ming, et al. A CNN-SVM-based fault identification method for high-voltage transmission lines[J]. Power System Protection and Control, 2022, 50 (13): 119- 125.
9	杨胡萍, 余阳, 汪超, 等. 基于VMD-CNN-BIGRU的电力系统短期负荷预测[J]. 中国电力, 2022, 55 (10): 71- 76.
	YANG Huping, YU Yang, WANG Chao, et al. Short-term load forecasting of power system based on VMD-CNN-BIGRU[J]. Electric Power, 2022, 55 (10): 71- 76.
10	高正男, 杨帆, 胡姝博, 等. 面向新能源电力系统状态估计的伪波动数据清洗[J]. 高电压技术, 2022, 48 (6): 2366- 2377.
	GAO Zhengnan, YANG Fan, HU Shubo, et al. Pseudo-fluctuation data cleaning for state estimation of new energy power system[J]. High Voltage Engineering, 2022, 48 (6): 2366- 2377.
11	曾囿钧, 肖先勇, 徐方维, 等. 基于CNN-BiGRU-NN模型的短期负荷预测方法[J]. 中国电力, 2021, 54 (9): 17- 23.
	ZENG Youjun, XIAO Xianyong, XU Fangwei, et al. A short-term load forecasting method based on CNN-BiGRU-NN model[J]. Electric Power, 2021, 54 (9): 17- 23.
12	何成兵, 王润泽, 张霄翔. 基于改进一维卷积神经网络的汽轮发电机组轴系扭振模态参数辨识[J]. 中国电机工程学报, 2020, 40 (S1): 195- 203.
	HE Chengbing, WANG Runze, ZHANG Xiaoxiang. Identification of torsional mode parameters of shaft system of turbo turbine generator sets based on improved one-dimensional convolutional neural network[J]. Proceedings of the CSEE, 2020, 40 (S1): 195- 203.
13	贾睿, 杨国华, 郑豪丰, 等. 基于自适应权重的CNN-LSTM&GRU组合风电功率预测方法[J]. 中国电力, 2022, 55 (5): 47- 56, 110.
	JIA Rui, YANG Guohua, ZHENG Haofeng, et al. Combined wind power prediction method based on CNN-LSTM & GRU with adaptive weights[J]. Electric Power, 2022, 55 (5): 47- 56, 110.
14	周艳真, 吴俊勇, 冀鲁豫, 等. 基于两阶段支持向量机的电力系统暂态稳定预测及预防控制[J]. 中国电机工程学报, 2018, 38 (1): 137- 147, 350.
	ZHOU Yanzhen, WU Junyong, JI Luyu, et al. Two-stage support vector machines for transient stability prediction and preventive control of power systems[J]. Proceedings of the CSEE, 2018, 38 (1): 137- 147, 350.
15	张若愚. 基于卷积神经网络的电力系统暂态稳定评估[D]. 北京: 北京交通大学, 2020.
	ZHANG Ruoyu. Power system transient stability assessment based on convolution neural network [D]. Beijing: Beijing Jiaotong University, 2020.
16	时纯, 刘君, 梁卓航, 等. 基于GAN和多通道CNN的电力系统暂态稳定评估[J]. 电网技术, 2022, 46 (8): 3191- 3202.
	SHI Chun, LIU Jun, LIANG Zhuohang, et al. Transient stability assessment of power system based on GAN and multi-channel CNN[J]. Power System Technology, 2022, 46 (8): 3191- 3202.
17	高昆仑, 杨帅, 刘思言, 等. 基于一维卷积神经网络的电力系统暂态稳定评估[J]. 电力系统自动化, 2019, 43 (12): 18- 26.
	GAO Kunlun, YANG Shuai, LIU Siyan, et al. Transient stability assessment for power system based on one-dimensional convolutional neural network[J]. Automation of Electric Power Systems, 2019, 43 (12): 18- 26.
18	李楠, 朱嫄, 崔莹. 考虑代价敏感的AC-LSTM暂态稳定评估[J]. 电力系统保护与控制, 2022, 50 (22): 160- 169.
	LI Nan, ZHU Yuan, CUI Ying. AC-LSTM transient stability assessment considering cost-sensitivity[J]. Power System Protection and Control, 2022, 50 (22): 160- 169.
19	孙黎霞, 白景涛, 周照宇, 等. 基于双向长短期记忆网络的电力系统暂态稳定评估[J]. 电力系统自动化, 2020, 44 (13): 64- 72.
	SUN Lixia, BAI Jingtao, ZHOU Zhaoyu, et al. Transient stability assessment of power system based on bi-directional long-short-term memory network[J]. Automation of Electric Power Systems, 2020, 44 (13): 64- 72.
20	吴思婕, 王怀远. 基于集成学习的时间自适应电力系统暂态稳定评估方法[J]. 电力系统保护与控制, 2022, 50 (24): 112- 119.
	WU Sijie, WANG Huaiyuan. Transient stability assessment of power system with time-adaptive method based on ensemble learning[J]. Power System Protection and Control, 2022, 50 (24): 112- 119.
21	苏童, 刘友波, 沈晓东, 等. 深度学习驱动的电力系统暂态稳定预防控制进化算法[J]. 中国电机工程学报, 2020, 40 (12): 3813- 3824.
	SU Tong, LIU Youbo, SHEN Xiaodong, et al. Deep learning-driven evolutionary algorithm for preventive control of power system transient stability[J]. Proceedings of the CSEE, 2020, 40 (12): 3813- 3824.
22	邵美阳, 吴俊勇, 李宝琴, 等. 基于两阶段集成深度置信网络的电力系统暂态稳定评估[J]. 电网技术, 2020, 44 (5): 1776- 1787.
	SHAO Meiyang, WU Junyong, LI Baoqin, et al. Transient stability assessment of power system based on two-stage ensemble deep belief network[J]. Power System Technology, 2020, 44 (5): 1776- 1787.
23	朱乔木, 党杰, 陈金富, 等. 基于深度置信网络的电力系统暂态稳定评估方法[J]. 中国电机工程学报, 2018, 38 (3): 735- 743.
	ZHU Qiaomu, DANG Jie, CHEN Jinfu, et al. A method for power system transient stability assessment based on deep belief networks[J]. Proceedings of the CSEE, 2018, 38 (3): 735- 743.
24	赵恺, 石立宝. 基于改进一维卷积神经网络的电力系统暂态稳定评估[J]. 电网技术, 2021, 45 (8): 2945- 2957.
	ZHAO Kai, SHI Libao. Transient stability assessment of power system based on improved one-dimensional convolutional neural network[J]. Power System Technology, 2021, 45 (8): 2945- 2957.
25	田芳, 周孝信, 史东宇, 等. 基于卷积神经网络的电力系统暂态稳定预防控制方法[J]. 电力系统保护与控制, 2020, 48 (18): 1- 8.
	TIAN Fang, ZHOU Xiaoxin, SHI Dongyu, et al. A preventive control method of power system transient stability based on a convolutional neural network[J]. Power System Protection and Control, 2020, 48 (18): 1- 8.

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