用于未来态预测的电网运行断面时空相似性挖掘

doi:10.19595/j.cnki.1000-6753.tces.211486

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (2031 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要运行状态有效预测可为电网风险预判与优化调控提供数据基础,对保障系统安全运行具有重要意义。该文提出一种用于未来态预测的电网运行断面时空相似性挖掘方法。首先,采用图表示学习算法对电网拓扑及其属性信息进行深层次无监督学习,提取表征运行断面空间特征的属性向量;然后,利用滑动时间窗算法将历史运行断面对应的空间特征向量按照不同时段划分到多个窗口;最后,从微观和宏观两角度计算不同窗口间对应样本相似性,获取与当前时段内断面最相似的一组连续断面,并将该组历史断面的后续时刻断面作为当前电网运行未来状态的参考。通过新英格兰10机39节点系统和实际电网算例进行验证,结果表明所提方法能够有效提取最相似的历史断面,进而实现对未来状态的辅助预测。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	顾雪平
	刘彤
	李少岩
	王铁强
	杨晓东

关键词 ：运行状态预测, 图表示学习算法, 运行断面空间特征, 时段划分, 相似性匹配

Abstract：Effective prediction of operation state can provide a data basis for power grid risk prediction and optimal regulation, which is of great significance for ensuring the system safe operation. This paper proposes a method for mining the temporal and spatial similarity of power grid running section for future state prediction. Firstly, the graph representation learning algorithm was used to deeply unsupervised learn the power grid topology and its attribute information, and the attribute vector representing the spatial features of running section was extracted. Further, the spatial feature vectors corresponding to the historical running sections was divided into multiple windows according to different time periods through the sliding time window algorithm. Finally, the similarity of corresponding samples between different windows was calculated from the microscopic and macroscopic perspectives to obtain a group of continuous sections that were most similar to the sections in the current period. The subsequent section of the most similar historical section was used as a reference for the future state of the current power grid. The results validated by IEEE 39-bus system and practical power grid simulation examples show that the proposed method can effectively match the most similar historical sections, then realize the auxiliary prediction of the future operation state.

Key words： Operation state prediction graph representation learning algorithm running section spatial feature period division similarity matching

收稿日期: 2021-09-23

PACS:

TM732

基金资助:国家电网公司科技项目资助（SGTYHT/17-JS-199）

通讯作者: 刘彤女,1996年生,博士研究生,研究方向为人工智能技术及其在电力系统中的应用、电力系统安全稳定评估与控制。E-mail：tongliu_1996@163.com

作者简介: 顾雪平男,1964年生,教授,博士生导师,研究方向为电力系统安全稳定评估与控制、电力系统安全防御与恢复控制、人工智能技术及其在电力系统中的应用等。E-mail：xpgu@ncepu.edu.cn

引用本文:

顾雪平, 刘彤, 李少岩, 王铁强, 杨晓东. 用于未来态预测的电网运行断面时空相似性挖掘[J]. 电工技术学报, 2022, 37(23): 6145-6156. Gu Xueping, Liu Tong, Li Shaoyan, Wang Tieqiang, Yang Xiaodong. Temporal and Spatial Similarity Mining of Power Grid Running Section for Future State Prediction. Transactions of China Electrotechnical Society, 2022, 37(23): 6145-6156.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.211486 https://dgjsxb.ces-transaction.com/CN/Y2022/V37/I23/6145

[1] 李婷, 胥威汀, 刘向龙, 等. 含高比例可再生能源的交直流混联电网规划技术研究综述[J]. 电力系统保护与控制, 2019, 47(12): 177-187.
Li Ting, Xu Weiting, Liu Xianglong, et al.Review on planning technology of AC/DC hybrid system with high proportion of renewable energy[J]. Power System Protection and Control, 2019, 47(12): 177-187.
[2] 王晨, 寇鹏. 基于卷积神经网络和简单循环单元集成模型的风电场内多风机风速预测[J]. 电工技术学报, 2020, 35(13): 2723-2735.
Wang Chen, Kou Peng.Wind speed forecasts of multiple wind turbines in a wind farm based on integration model built by convdutional neural network and simple recurrent unit[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2723-2735.
[3] 金红洋, 滕云, 冷欧阳, 等. 基于源荷不确定性状态感知的无废城市多能源协调储能模型[J]. 电工技术学报, 2020, 35(13): 2830-2842.
Jin Hongyang, Teng Yun, Leng Ouyang, et al.Multi-energy coordinated energy storage model in zero-waste cities based on situation awareness of source and load uncertainty[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2830-2842.
[4] 王涛, 张尚, 顾雪平, 等. 电网运行状态的趋势辨识[J]. 电工技术学报, 2015, 30(24): 171-180.
Wang Tao, Zhang Shang, Gu Xueping, et al.Trends identification of power system operating states[J]. Transactions of China Electrotechnical Society, 2015, 30(24): 171-180.
[5] 梁海平, 田潮, 王铁强, 等. 基于改进K-means算法的电网运行断面相似性匹配研究[J]. 电力自动化设备, 2019, 39(7): 119-124, 140.
Liang Haiping, Tian Chao, Wang Tieqiang, et al.Running section similarity matching based on improved K-means algorithm[J]. Electric Power Automation Equipment, 2019, 39(7): 119-124, 140.
[6] 王铁强, 鲁鹏, 曹欣, 等. 基于堆叠自动编码器的电网运行断面相似性匹配研究[J]. 电力建设, 2021, 42(1): 117-124.
Wang Tieqiang, Lu Peng, Cao Xin, et al.Research on similarity matching of power network operation section applying stacked automatic encoder[J]. Electric Power Construction, 2021, 42(1): 117-124.
[7] 郭烨, 吴文传, 张伯明, 等. 潮流预报在电网安全预警系统中的应用[J]. 电力系统自动化, 2010, 34(5): 107-111.
Guo Ye, Wu Wenchuan, Zhang Boming, et al.Power flow forecasting method and its application in early warning and security countermeasure system[J]. Automation of Electric Power Systems, 2010, 34(5): 107-111.
[8] Ahmed A, McFadden F J S, Rayudu R. Weather-dependent power flow algorithm for accurate power system analysis under variable weather conditions[J]. IEEE Transactions on Power System, 2019, 34(4): 2719-2729.
[9] 黄宇鹏, 李立新, 王磊, 等. 基于神经网络的电网运行趋势估计算法及应用[J]. 智慧电力, 2019, 47(6): 113-120.
Huang Yupeng, Li Lixin, Wang Lei, et al.Power network operation trend estimation algorithm based on neutral network and its application[J]. Smart Grid, 2019, 47(6): 113-120.
[10] 王毅, 侯俊贤, 马世英, 等. 用于调度计划安全稳定校核的潮流数据自动整合调整方法[J]. 电网技术, 2010, 34(4): 100-104.
Wang Yi, Hou Junxian, Ma Shiying, et al.A method of automatic integration and regulation of power flow data for security and stability check of generation scheduling analysis[J]. Power System Technology, 2010, 34(4): 100-104.
[11] 潘超, 李润宇, 蔡国伟, 等. 基于时空关联分解重构的风速超短期预测[J]. 电工技术学报, 2021, 36(22): 4739-4748.
Pan Chao, Li Runyu, Cai Guowei, et al.Multi-step ultra-short-term wind speed prediction based on decomposition and reconstruction of time-spatial correlation[J]. Transactions of China Electrotechnical Society, 2021, 36(22): 4739-4748.
[12] Zhou Mike, Yan Jianfeng, Zhou Xiaoxin.Real-time online analysis of power grid[J]. CSEE Journal of Power and Energy Systems, 2020, 6(1): 236-238.
[13] 涂存超, 杨成, 刘知远, 等. 网络表示学习综述[J].中国科学: 信息科学, 2017, 47(8): 32-48.
Tu Chaocun, Yang Cheng, Liu Zhiyuan, et al.Network representation learning: an overview[J]. Scientia Sinica (Informationis), 2017, 47(8): 32-48.
[14] Wang Jian, Wang Xi, Ma Chaoqun, et al.A survey on the development status and application prospects of knowledge graph in smart grids[J]. IET Generation, Transmission & Distribution, 2020, 15(3): 383-407.
[15] 杨茂, 张罗宾. 基于数据驱动的超短期风电功率预测综述[J]. 电力系统保护与控制, 2019, 47(13): 171-186.
Yang Mao, Zhang Luobin.Review on ultra-short term wind power forecasting based on data-driven approach[J]. Power System Protection and Control, 2019, 47(13): 171-186.
[16] 李兵洋, 肖健梅, 王锡淮. 融合邻域粗糙约简与深度森林的电力系统暂态稳定评估[J]. 电工技术学报, 2020, 35(15): 3245-3257.
Li Bingyang, Xiao Jianmei, Wang Xihuai.Power system transient stability assessment based on hybrid neighborhood rough reduction and deep forest[J]. Transactions of China Electrotechnical Society, 2020, 35(15): 3245-3257.
[17] Zhou Fan, Yang Qing, Zhong Ting, et al.Variational graph neural networks for road traffic prediction in intelligent transportation systems[J]. IEEE Transaction on Industrial Informatics, 2021, 17(4): 2802-2812.
[18] Santoro A, Raposo D, Barrett D G T. et al. A simple neural network module for relational reasoning[C]// 31st Annual Conference on Neural Information Processing Systems, California, 2017: 4968-4977.
[19] 钟智, 管霖, 苏寅生, 等. 基于图注意力深度网络的电力系统暂态稳定评估[J]. 电网技术, 2021, 45(6): 2122-2130.
Zhong Zhi, Guan Lin, Su Yinsheng, et al.Power system transient stability assessment based on graph attention deep network[J]. Power System Technology, 2021, 45(6): 2122-2130.
[20] 李佳玮, 王小君, 和敬涵, 等. 基于图注意力网络的配电网故障定位方法[J]. 电网技术, 2021, 45(6): 2113-2121.
Li Jiawei, Wang Xiaojun, He Jinghan, et al.Distribution network fault location based on graph attention network[J]. Power System Technology, 2021, 45(6): 2113-2121.
[21] Wu Yiming, Bourahla O E F, Li Xi, et al. Adaptive graph representation learning for video person re-identification[J]. IEEE Transaction on Image Processing, 2020, 25(29): 8821-8830.
[22] 孙伟, 朱世睿, 杨建平, 等. 基于图卷积网络的微电网拓扑辨识[J/OL]. 电力系统自动化, 2021: 1-11. http://kns.cnki.net/kcms/detail/32.1180.TP.20211217.1327.002.html.
Sun Wei, Zhu Shirui, Yang Jianping, et al. Topology identification of microgrid based on graph convolution network[J/OL]. Automation of Electric Power Systems, 2021: 1-11. http://kns.cnki.net/kcms/detail/32.1180.TP.20211217.1327.002.html.
[23] Hamiton W L, Ying R, Leskovec J.Inductive representation learning on large graphs[C]// 31st Annual Conference on Neural Information Processing Systems, California, 2017: 1025-1035.
[24] 陈俊生, 李剑, 陈伟根, 等. 采用滑动窗口及多重加噪比堆栈降噪自编码的风电机组状态异常检测方法[J]. 电工技术学报, 2020, 35(2): 346-358.
Chen Junsheng, Li Jian, Chen Weigen, et al.A method for detecting anomaly conditions of wind turbines using stacked denoising autoencoders with sliding window and multiple noise ratios[J]. Transactions of China Electrotechnical Society, 2020, 35(2): 346-358.
[25] Abdolah S, Babak M, Jagdish Patra C.Sliding-window-based real-time model order reduction for stability prediction in smart grid[J]. IEEE Transactions on Power System, 2019, 34(1): 326-337.