Online Estimation of Power Shortage in Power Systems Driven by Local Frequency Measurement Data

doi:10.16183/j.cnki.jsjtu.2021.401

Abstract

Abstract:

With the growing penetration of renewable generation, the inertia and frequency support ability of the renewable-dominated power system are continuously reduced, which leads to frequency collapse when the system is disturbed. Therefore, it is of great significance to promptly and accurately evaluate the power shortage after a major disturbance to fill the power shortage quickly. This paper proposes an online estimation approach of power shortage in power system based on deep convolution and long-short term memory composite neural network driven by local frequency measurement data. First, since using the synchronous measurements to obtain the frequency of center of inertia (COI) cannot adapt to the rapidity of online estimation, this paper employs the local frequency measurements to estimate the COI frequency, avoiding the delay effect caused by the complex communication. Then, it designs a deep composite neural network to mine the correlation information between massive frequency data and power shortage. Finally, it tests the simulations on a 39-bus system to verify the effectiveness of the proposed approach. The results demonstrate that the proposed approach is effective and fast.

Key words: renewable-dominated power system, frequency response, composite neural network, center of inertia (COI), data-driven

CLC Number:

TM712

TANG Zhen, HAO Lihua, FENG Jing. Online Estimation of Power Shortage in Power Systems Driven by Local Frequency Measurement Data[J]. Journal of Shanghai Jiao Tong University, 2023, 57(4): 403-411.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Tab.1

Fig.6

Fig.7

Fig.8

Tab 2

Fig.9

Fig.10

References 21

[1]	JIN C, LI W, SHEN J, et al. Active frequency response based on model predictive control for bulk power system[J]. IEEE Transactions on Power Systems, 2019, 34(4): 3002-3013. doi: 10.1109/TPWRS.59 URL
[2]	王博, 杨德友, 蔡国伟. 高比例新能源接入下电力系统惯量相关问题研究综述[J]. 电网技术, 2020, 44(8): 2998-3006.
	WANG Bo, YANG Deyou, CAI Guowei. Review of research on power system inertia related issues in the context of high penetration of renewable power generation[J]. Power System Technology, 2020, 44(8): 2998-3006.
[3]	孙华东, 王宝财, 李文锋, 等. 高比例电力电子电力系统频率响应的惯量体系研究[J]. 中国电机工程学报, 2020, 40(16): 5179-5191.
	SUN Huadong, WANG Baocai, LI Wenfeng, et al. Research on inertia system of frequency response for power system with high penetration electronics[J]. Proceedings of the Chinese Society of Electrical Engineering. 2020, 40(16): 5179-5191.
[4]	丁明, 王伟胜, 王秀丽, 等. 大规模光伏发电对电力系统影响综述[J]. 中国电机工程学报, 2014, 34(1): 1-14.
	DING Ming, WANG Weisheng, WANG Xiuli, et al. A review on the effect of large-scale PV generation on power systems[J]. Proceedings of the Chinese Society of Electrical Engineering, 2014, 34(1): 1-14.
[5]	XIN H, LIU Y, WANG Z, et al. A new frequency regulation strategy for photovoltaic systems without energy storage[J]. IEEE Transactions on Sustainable Energy, 2013, 4(4): 985-993. doi: 10.1109/TSTE.2013.2261567 URL
[6]	董天翔, 翟保豫, 李星, 等. 风储联合系统参与频率响应的优化控制策略[J]. 电网技术, 2022, 46(10): 3980-3989.
	DONG Tianxiang, ZHAI Baoyu, LI Xing, et al. Optimal control strategy for combined wind-storage system to participate in frequency response[J]. Power System Technology, 2022, 46(10): 3980-3989.
[7]	ZHANG Y, ZHAO C, TANG W, et al. Profit-maximizing planning and control of battery energy storage systems for primary frequency control[J]. IEEE Transactions on Smart Grid, 2018, 9(2): 712-723. doi: 10.1109/TSG.5165411 URL
[8]	常喜强, 何恒靖, 解大, 等. 计及频率差变化率的低频减载方案的研究[J]. 电力系统保护与控制, 2010, 38(4): 68-73.
	CHANG Xiqiang, HE Hengjing, XIE Da, et al. Study on under frequency load shedding schemes concerning ROCOF[J]. Power System Protection and Control, 2010, 38(4): 68-73.
[9]	TERZIJA V. Adaptive underfrequency load shedding based on the magnitude of the disturbance estimation[J]. IEEE Transactions on Power Systems, 2006, 21(3): 1260-1266. doi: 10.1109/TPWRS.2006.879315 URL
[10]	何培灿, 温步瀛, 王怀远. 计及暂态电压稳定性的自适应低频减载方案[J]. 福州大学学报(自然科学版), 2019, 47(6): 765-770.
	HE Peican, WEN Buying, WANG Huaiyuan. Adaptive under frequency load shedding scheme considering transient voltage stability[J]. Journal of Fuzhou University (Natural Science Edition), 2019, 47(6): 765-770.
[11]	HE P, WEN B, WANG H. Decentralized adaptive under frequency load shedding scheme based on load information[J]. IEEE Access, 2019, 7: 52007-52014. doi: 10.1109/Access.6287639 URL
[12]	TUTTELBERG K, KILTER J, WILSON D, et al. Estimation of power system inertia from ambient wide area measurements[J]. IEEE Transactions on Power Systems, 2018, 33(6): 7249-7257. doi: 10.1109/TPWRS.2018.2843381 URL
[13]	WALL P, TERZIJA V. Simultaneous estimation of the time of disturbance and inertia in power systems[J]. IEEE Transactions on Power Delivery, 2014, 29(4): 2018-2031. doi: 10.1109/TPWRD.2014.2306062 URL
[14]	ZOGRAFOS D, GHANDHARI M, ERIKSSON R. Power system inertia estimation: Utilization of frequency and voltage response after a disturbance[J]. Electric Power Systems Research, 2018, 161: 52-60. doi: 10.1016/j.epsr.2018.04.008 URL
[15]	文云峰, 杨伟峰, 林晓煌. 低惯量电力系统频率稳定分析与控制研究综述及展望[J]. 电力自动化设备, 2020, 40(9): 211-222.
	WEN Yunfeng, YANG Weifeng, LIN Xiaohuang. Review and prospect of frequency stability analysis and control of low-inertia power systems[J]. Electric Power Automation Equipment, 2020, 40(9): 211-222.
[16]	周海锋, 倪腊琴, 徐泰山. 电力系统功率频率动态特性研究[J]. 电网技术, 2009, 33(16): 58-62.
	ZHOU Haifeng, NI Laqin, XU Taishan. Study on power-frequency dynamic characteristic of power grid[J]. Power System Technology, 2009, 33(16): 58-62.
[17]	徐舒玮, 邱才明, 张东霞, 等. 基于深度学习的输电线路故障类型辨识[J]. 中国电机工程学报, 2019, 39(1): 65-74.
	XU Shuwei, QIU Caiming, ZHANG Dongxia, et al. A deep learning approach for fault type identification of transmission line[J]. Proceedings of the Chinese Society of Electrical Engineering, 2019, 39(1): 65-74.
[18]	颜波, 张磊, 褚学宁. 基于卷积神经网络的用户感知评估建模[J]. 上海交通大学学报, 2019, 53(7): 844-851.
	YAN Bo, ZHANG Lei, CHU Xuening. User experience evaluation modeling based on convolutional neural network[J]. Journal of Shanghai Jiao Tong University, 2019, 53(7): 844-851.
[19]	张宇帆, 艾芊, 林琳, 等. 基于深度长短时记忆网络的区域级超短期负荷预测方法[J]. 电网技术, 2019, 43(6): 1884-1891.
	ZHANG Yufan, AI Qian, LIN Lin, et al. A very short-term load forecasting method based on deep LSTM RNN at zone level[J]. Power System Technology, 2019, 43(6): 1884-1891.
[20]	石敏, 蔡少委, 易清明. 基于空洞-稠密网络的交通拥堵预测模型[J]. 上海交通大学学报, 2021, 55(2): 124-130.
	SHI Min, CAI Shaowei, YI Qingming. A traffic congestion prediction model based on dilated-dense network[J]. Journal of Shanghai Jiao Tong University, 2021, 55(2): 124-130.
[21]	高嵩, 陆倚鹏, 王笑倩, 等. 基于深度学习的悬式瓷绝缘子红外图像识别方法[J]. 电力科学与技术学报, 2020, 35(5): 119-125.
	GAO Song, LU Yipeng, WANG Xiaoqian, et al. Infrared image recognition method of porcelain disc-suspended insulators based on deep learning technology[J]. Journal of Electric Power Science and Technology, 2020, 35(5): 119-125.

序号	网络层	卷积核		数量	输出大小	是否全0 填充
序号	网络层	大小	步长	数量	输出大小	是否全0 填充
1	输入层	—	—	—	50×1	否
2	卷积层1	5×1	1	64	50×64	是
3	卷积层2	5×1	1	128	50×128	是
4	池化层1	2×1	1	—	25×128	否
5	卷积层3	5×1	1	256	25×256	是
6	卷积层4	5×1	1	512	25×512	是
7	池化层2	2×1	1	—	12×256	否
8	LSTM1	—	—	80	80×1	否
9	LSTM2	—	—	60	60×1	否
10	输出层	—	—	—	1×1	否

方法	MAPE	NRE	MRE	E<5% 的概率/%	模型训练时间/s	一组数据测试时间/ ms
方法1	21.35	0.76	63.81	14.5	—	—
DCNN	3.11	0	13.91	81.0	1 914.8	0.77
BP	7.10	0	29.27	49.0	1 699.7	0.97
LSTN	3.87	0	14.80	72.5	1 988.0	1.02
DCNN- LSTM	2.74	0	11.40	87.5	2 014.8	1.32