| 15 | 0 | 1396 |
| 下载次数 | 被引频次 | 阅读次数 |
针对数据缺失时暂态电压稳定评估模型精度下降的问题，提出一种基于多视图缺失数据填充和门控图神经网络的电力系统暂态电压稳定评估方法。首先，基于多视图互补的时空视图来填充缺失数据，得到完整的数据集；然后，采用修复完整的数据集训练门控图神经网络模型进行暂态电压稳定评估，评估模型要进行快速更新，以提高在线应用的性能；最后，在IEEE39节点系统算例上进行验证所提方法的有效性。仿真结果表明，本文方法可以在任何同步向量测量单元放置信息丢失和网络拓扑变化的情况下及时有效地填补缺失数据，且所用评估模型的评估性能具有显著优势。
Abstract：Aiming at the problem of decreased accuracy of transient voltage stability assessment model when data is missing, the author proposes a transient voltage stability assessment method for power system based on multi - view missing data filling and gating graph neural network. Firstly, the missing data are filled based on the complementary spatio - temporal views of multiple views to obtain a complete dataset. Then, the restored complete dataset is used to train the gating graph neural network model for transient voltage stability assessment, and the assessment model should be updated quickly to improve the performance of online applications. Finally, the effectiveness of the proposed method is verified on the IEEE39- node system example. The simulation results show that the proposed method can fill the missing data in a timely and effective manner in case of any synchronization vector measurement unit placement information loss and network topology changes, and the evaluation performance of the used evaluation model has significant advantages.
[1] 张雅婷,刘颂凯,张磊,等.针对数据缺失的电力系统暂态稳定评估方法[J].电力系统及其自动化学报, 2023, 35(3):59-68. ZHANG Yating, LIU Songkai, ZHANG Lei, et al. A Method of Power System Transient Stability Assessment based on Missing Data[J]. Proceedings of the CSU-EPSA, 2023, 35(3):59-68.
[2] 史法顺,吴俊勇,吴昊衍,等.基于深度学习的电力系统暂态功角与暂态电压稳定裕度一体化评估[J].电网技术, 2023, 47(2):731-740. SHI Fashun, WU Junyong, WU Haoyan, et al. Integrated Evaluation of Power System Transient Power Angle and Transient Voltage Stability Margin Based on Deep Learning[J]. Power System Technology, 2023, 47(2):731-740.
[3] 陈康,王泽,郭永吉.基于grcForest模型的风电并网系统暂态电压稳定评估[J].智慧电力, 2023, 51(1):31-37. CHEN Kang, WANG Ze, GUO Yongji. Transient Voltage Stability Assessment of Wind Power Grid-connected System Based on grcForest Model[J]. Smart Power, 2023, 51(1):31-37.
[4] 白雪峰,倪以信.电力系统动态安全分析综述[J].电网技术, 2004(16):14-20. BAI Xuefeng, NI Yixin. Survey on Dynamic Security Assessment of Power Systems[J]. Power System Technology, 2004, 28(16):14-20.
[5] ZHANG Yuchen, XU Yan, DONG Zhaoyang, et al. A Hierarchical Self-adaptive Data-analytics Method for Real-time Power System Short-term Voltage Stability Assessment[J]. IEEE Transactions on Industrial Informatics, 2019, 15(1):74-84.
[6] 季佳伸,吴俊勇,王彦博,等.基于深度残差网络的电力系统暂态电压稳定评估[J].电网技术, 2022, 46(7):2500-2509. JI Jiashen, WU Junyong, WANG Yanbo, et al. Power System Transient Voltage Stability Assessment Based on Deep Residual Network[J]. Power System Technology, 2022, 46(7):2500-2509.
[7] 朱瑞金,董亚丽,唐波.基于改进深度森林的暂态电压稳定快速评估[J].电网与清洁能源, 2022, 38(6):24-30, 43. ZHU Ruijin, DONG Yali, TANG Bo. Fast Assessment of Transient Voltage Stability Based on Improved Deep Forest[J]. Power System and Clean Energy, 2022, 38(6):24-30, 43.
[8] 陈达,朱林,张健,等.基于卷积神经网络的暂态电压稳定评估及风险量化[J].电力系统自动化, 2021, 45(14):65-71. CHEN Da, ZHU Lin, ZHANG Jian, et al. Transient Voltage Stability Assessment and Risk Quantification Based on Convolutional Neural Network[J]. Automation of Electric Power Systems, 2021, 45(14):65-71.
[9] L. Zhu, C. Lu, Y. Z. Sun. Time Series Shapelet Classification based Online Short-term Voltage Stability Assessment[J]. IEEE Transactions on Power Systems, 2016, 31(2):1-10.
[10] H. Mohammadi, G. Khademi, M. Dehghani, et al. Voltage Stability Assessment Using Multi-objective Biogeography-based Subset Selection[J]. International Journal of Electrical Power&Energy Systems, 2018, 103:525-536.
[11] J. J. Q. Yu, A. Y. S. Lam, D. J. Hill, et al. Delay Aware Power System Synchrophasor Recovery and Prediction Framework[J]. IEEE Transactions on Smart Grid, 2019, 10(4):3732-3742.
[12] M. He, V. Vittal, J. S. Zhang. Online Dynamic Security Assessment with Missing PMU Measurements:A Data Mining Approach[J]. IEEE Transactions on Power Systems, 2013, 28(2):1969-1977.
[13] 潘晓杰,徐友平,朱成亮,等.基于深度学习的多输入特征融合的暂态电压稳定性评估方法[J].电网与清洁能源, 2021, 37(2):79-84. PAN Xiaojie, XU Youping, ZHU Chengliang, et al. Transient Voltage Stability Evaluation Method Based on Multi-Input Feature Fusion of Deep Learning[J]. Power System and Clean Energy, 2021, 37(2):79-84.
[14] M. Wu, L. Xie. Online Detection of Low-quality Synchrophasor Measurements:A Data-driven Approach[J]. IEEE Transactions on Power Systems, 2017, 32(4):2817-2827.
[15] 杜一星,胡志坚,王方洲,等.基于受扰后量测响应信息的两阶段电力系统暂态稳定评估[J].电网技术, 2022, 46(6):2230-2240. DU Yixing, HU Zhijian, WANG Fangzhou, et al. Two-stage Power System Transient Stability Assessment Based on Post-disturbance Measurement Response Information[J]. Power System Technology, 2022, 46(6):2230-2240.
[16] ZHOU XM, GUAN X, SUN D, et al. Transient Stability Assessment Based on Gated Graph Neural Network With Imbalanced Data in Internet of Energy[J]. IEEE Internet of Things Journal, 2022, 9(12):9320-9331.
[17] 魏文兵,毛钧毅,荣娜,等.采用随机矩阵与CNN的暂态电压稳定快速评估[J].电网与清洁能源, 2020, 36(11):68-76. WEI Wenbing, MAO Junyi, RONG Na, et al. Fast Evaluation of Transient Voltage Stability Using Random Matrix and Convolutional Neural Network[J]. Power System and Clean Energy, 2020, 36(11):68-76.
[18] 杨维全,朱元振,刘玉田.基于卷积神经网络的暂态电压稳定快速评估[J].电力系统自动化, 2019, 43(22):46-51, 136. YANG Weiquan, ZHU Yuanzhen, LIU Yutian. Fast Assessment of Transient Voltage Stability Based on Convolutional Neural Network[J]. Automation of Electric Power Systems, 2019, 43(22):46-51, 136.
[19] 朱林,张健,陈达,等.面向暂态电压稳定评估的卷积神经网络输入特征构建方法[J].电力系统自动化, 2022, 46(1):85-93. ZHU Lin, ZHANG Jian, CHEN Da, et al. Construction Method for Input Features of Convolutional Neural Network for Transient Voltage Stability Assessment[J]. Automation of Electric Power Systems, 2022, 46(1):85-93.
[20] 张晓英,史冬雪,张琎,等.基于CPSO-BP神经网络的风电并网暂态电压稳定评估[J].智慧电力, 2021, 49(10):38-44. ZHANG Xiaoying, SHI Dongxue, ZHANG Jin, et al. Transient Voltage Stability Assessment of Power System Integrated with Wind Power Based on CPSO-BP Neural Network[J]. Smart Power, 2021, 49(10):38-44.
[21] 陈小杰.一种优化权重的k-近邻填补缺失值的算法研究[J].无线互联科技, 2022, 19(8):121-125. CHEN Xiaojie. Research on A K-nearest Neighbor Algorithm in Filling Missing Data with Optimal Weight[J]. Wireless Internet Technology, 2022, 19(8):121-125.
基本信息:
DOI：10.19929/j.cnki.nmgdljs.2024.0005
中图分类号:
引用信息:
[1]姜鸣瞻1, 杨楚原1, 蒋何为1等.针对电力系统数据缺失的暂态电压稳定评估方法[J].内蒙古电力技术,2024,42(01):27-32.DOI:10.19929/j.cnki.nmgdljs.2024.0005.
基金信息:
国家自然科学基金“计及热量迁移动态过程的电热耦合系统时空异构动态优化调度方法研究”（52007103）