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基于卷积神经网络的暂态电压稳定快速评估
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作者单位:

电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061

摘要:

随着特高压直流输电的发展和负荷构成及特性的变化,暂态电压问题严重威胁系统的安全稳定运行。基于卷积神经网络(CNN),提出一种交直流受端电网分区暂态电压稳定快速评估方法。计及系统快速动态响应元件影响,基于暂态电压时序信息构建暂态电压跌落面积矩阵,利用基于t分布的随机近邻嵌入(t-SNE)算法将其映射到二维平面,对受端电网进行分区。依据节点相对距离选择各分区稳态潮流特征。构建线路故障严重度指标,据其对故障线路号进行编码,将编码结果与故障线路号共同作为故障特征。采用粒子群优化算法确定各分区CNN最优卷积核大小和数量,提升CNN性能。实际多馈入交直流电网的仿真结果表明了方法的有效性。

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基金项目:

国家重点研发计划资助项目(2017YFB0902600);国家电网公司科技项目(SGJS0000DKJS1700840)

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Fast Assessment of Transient Voltage Stability Based on Convolutional Neural Network
Author:
Affiliation:

Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong University), Jinan 250061, China

Abstract:

With the development of ultra-high-voltage direct current(HVDC)and the changes of load composition and characteristics, the safe and stable operation of power system is seriously threatened by transient voltage problem. Based on convolutional neural network(CNN), a fast assessment method for partitioning transient voltage stability of AC/DC receiving-end power grid is proposed. Considering the influence of fast dynamic response components, the transient voltage sag area matrix is set up based on the time sequence information of transient voltage. This matrix is mapped into two-dimensional plane to divide the receiving-end power grid into several partitions based on the t-stochastic neighbor embedding(t-SNE)algorithm. The steady power flow features of each partition are selected according to relative distance of buses. Fault severity index is constructed and the fault line number is coded based on the order of fault severity index. The coding result and fault line number are taken as fault features. The particle swarm optimization(PSO)algorithm is adopted to determine the optimal size and number of convolution kernel in each partition of CNN to improve the performance of CNN. Simulation results of a real multi-infeed AC/DC power system show the effectiveness of the proposed method. This work is supported by National Key R&D Program of China(No. 2017YFB0902600)and State Grid Corporation of China(No. SGJS0000DKJS1700840).

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引用本文
[1]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. DOI:10.7500/AEPS20190430037.
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. DOI:10.7500/AEPS20190430037.
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  • 收稿日期:2019-04-30
  • 最后修改日期:2019-10-13
  • 录用日期:2019-09-17
  • 在线发布日期: 2019-10-10
  • 出版日期: 2019-11-25
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