上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (4): 403-411.doi: 10.16183/j.cnki.jsjtu.2021.401
所属专题: 《上海交通大学学报》2023年“新型电力系统与综合能源”专题
收稿日期:
2021-10-12
修回日期:
2022-04-05
接受日期:
2022-04-25
出版日期:
2023-04-28
发布日期:
2023-05-05
作者简介:
唐 震(1966-),教授级高级工程师,从事继电保护试验研究、电力系统仿真分析研究.电话(Tel.):0351-4263031;E-mail:基金资助:
TANG Zhen1(), HAO Lihua1, FENG Jing2
Received:
2021-10-12
Revised:
2022-04-05
Accepted:
2022-04-25
Online:
2023-04-28
Published:
2023-05-05
摘要:
随着新能源并网比例的不断提高,新型电力系统的惯量及频率支撑能力不断降低,导致系统在遭受扰动时易出现频率崩溃,因此需要对扰动后系统功率缺额进行快速准确评估以用于功率缺额的快速填补.提出了一种局部频率测量数据驱动的基于深度卷积和长短期记忆复合神经网络的系统功率缺额在线评估方法.首先,由于同步测量获取实际惯量中心 (COI) 频率无法适应在线评估的快速性,所以利用局部测量频率估算得到COI频率,避免了复杂通信造成的延时效应;然后设计了一种深度复合神经网络,挖掘海量频率数据和功率缺额间的关联信息;最后搭建39节点系统进行仿真验证,结果显示了所提方法的有效性和快速性.
中图分类号:
唐震, 郝丽花, 冯静. 局部频率测量数据驱动的电力系统功率缺额在线评估方法[J]. 上海交通大学学报, 2023, 57(4): 403-411.
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.
[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. |
[1] | 于淼, 胡敬轩, 张寿志, 魏静静, 孙建群, 吴屹潇. 基于PMU梯度动态偏差的新型电力系统快速稳定性[J]. 上海交通大学学报, 2024, 58(1): 40-49. |
[2] | 张硕, 李薇, 李英姿, 刘强, 曾鸣. 面向新型电力系统的可再生能源绿色电力证书差异化配置模型[J]. 上海交通大学学报, 2022, 56(12): 1561-1571. |
[3] | 张恒睿. 需求导向的材料设计[J]. 上海交通大学学报, 2021, 55(Sup.1): 93-94. |
[4] | 巩伟峥, 许凌, 姚寅. 计及风速分布与机组惯量转化不确定性的风电场可用惯量估计[J]. 上海交通大学学报, 2021, 55(S2): 51-59. |
[5] | 李勇, 张梦骏, 仇栋, 范云锋, 苏智勇, 邱令存. 数据驱动的指控系统增强现实电子沙盘设计与开发[J]. 空天防御, 2021, 4(2): 27-. |
[6] | 徐巧宁,艾青林,杜学文,刘毅. 模型-数据联合驱动的船舶舵机电液伺服系统早期故障检测[J]. 上海交通大学学报, 2020, 54(5): 451-464. |
[7] | 王峥1,褚学宁1,陈汉斯1,张磊1,颜波1,刘航2. 运行数据驱动的手机性能需求推断与感知分析[J]. 上海交通大学学报(自然版), 2018, 52(7): 777-783. |
[8] | 于洪洁,董奕煊. 由Rossler混沌系统构建的复合型网络的同步[J]. 上海交通大学学报(自然版), 2018, 52(12): 1559-1564. |
[9] | 邵昊舒,蔡旭. 大型风电机组惯量控制研究现状与展望[J]. 上海交通大学学报(自然版), 2018, 52(10): 1166-1177. |
[10] | 张光明, 李柠, 李少远. 一种数据驱动的预测控制器性能监控方法[J]. 上海交通大学学报(自然版), 2011, 45(08): 1113-1118. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||