采用多层次特征融合SPP-net的暂态稳定多任务预测
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贵州大学

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TP273

基金项目:

国家自然科学基金(51567006); 贵州省普通高等高校科技拔尖人才支持计划资助(2018036); 贵州省科学技术基金(黔科合基础[2019]1100);贵州省科技创新人才团队项目([2018]5615)


Multi-task Prediction for Transient Stability using Multi-level Feature Fusion based SPP-net
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Affiliation:

Guizhou University

Fund Project:

National Nature Science Foundation under Grant (No.51567006);Sponsored by Program for Top Science&Technology Talents in Universities of Guizhou Province(2018036);Guizhou Province Science and Technology Fund ([2019]1100);Guizhou Province Science and Technology Innovation Talent Team Project ([2018] 5615)

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    摘要:

    为提升基于卷积神经网络(CNN)的电力系统暂态稳定预测精度并呈现暂态稳定类别、程度等更全面的预测结果,本文提出了一种基于多层次特征融合空间金字塔池化网络(multi-level feature fusion based SPP-net, MSPP-net)的暂态稳定多任务预测方法。首先,通过同步相量测量装置(phasor measurement units, PMUs)获取故障清除后各发电机功角、机端母线电压幅值及相角数据,进而构造出一个三维输入数据矩阵。其次,在CNN的基础上采用空间金字塔池化层提取高层特征不同尺寸信息,并通过跳跃链接获取不同卷积层特征信息,进而得到丰富的多尺度、多层次融合特征。最后,通过硬参数共享机制建立一个涉及分类与回归的CNN多任务学习模型,以实现电力系统暂态稳定性判断、临界发电机识别和稳定裕度预测。借助MATLAB R2020a和PST 3.0软件,案例分析在一个IEEE 10机39母线系统和一个IEEE 50机145母线系统中展开。与主要传统浅层网络和深度学习方法的比较结果表明了本文所提方法的有效性和更优的预测性能,并验证了所提方法在噪声环境或PMUs非100%覆盖条件下的适用性。

    Abstract:

    In order to improve the accuracy of transient stability predictions based on convolutional neural networks (CNN) and demonstrate multi-angle auxiliary decision-making information, such as transient stability classification and margin, etc., this paper proposes a multi-task model for transient stability prediction using multi-level feature fusion based spatial pyramid pooling convolutional network. Firstly, the short-time disturbed trajectories of each generator can be obtained by phasor measurement units (PMUs), and a three-dimension information matrix may be constructed using these trajectories. Furthermore, the space pyramid pooling layer and multi-level pooling layer could be employed for extracting multi-scale and multi-level feature based on the CNN, which can obtain more comprehensive and detailed information of input data. Finally, the multi-task learning model with classification and regression will be built by hard parameter sharing. It may achieve transient stability classi?cation, critical generators identification and stability margin prediction. The case studies have been carried on an IEEE 145-bus system and an IEEE 39-bus system with the help of Matlab R2020a and PST 3.0 software. The results in comparison with those results from the conventional methods show that the proposed methodology is valid, and it shows the applicability when the information of PUMs is incomplete or contains noise.

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  • 收稿日期:2020-11-13
  • 最后修改日期:2021-03-29
  • 录用日期:2021-04-07
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