A Hierarchical Self-Adaptive Method for Post-Disturbance Transient Stability Assessment of Power Systems Using an Integrated CNN-Based Ensemble Classifie

A Hierarchical Self-Adaptive Method for Post-Disturbance Transient Stability Assessment of Power Systems Using an Integrated CNN-Based Ensemble Classifie
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使用基于 CNN 的集成集成分类的电力系统扰后暂态稳定性评估的分层自适应方法

DOI：
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发表时间：
2019
期刊：
energies
影响因子：
3.2
通讯作者：
Meiyang Shao
Meiyang Shao
中科院分区：
工程技术4区
文献类型：
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作者：
Ruoyu Zhang;Junyong Wu;Yan Xu;Baoqin Li;Meiyang Shao

文献摘要

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数据驱动的方法使用同步或测量在暂态稳定性评估（TSA）中扮演着重要的角色。对于扰动后TSA，对于响应时间应该有多长没有明确的结论。此外，以往的研究很少考虑预测结果的置信水平和具体的稳定程度。由于暂态稳定的发展速度非常快，造成的经济损失也非常巨大，因此迫切需要更快的响应速度、可靠准确的预测结果和特定的稳定度。提出了一种基于卷积神经网络（CNN）集成分类器的分层自适应方法来解决这些问题。首先，一组分类器在不同的响应时间被顺序地组织起来，以构造所提出的方法的不同的层。其次，定义了置信度综合决策规则。那些预测为可信的稳定/不稳定的情况下，被发送到稳定/不稳定的回归模型，这是建立在相应的决策时间。仿真结果表明，该方法不仅可以平衡暂态稳定预测的准确性和快速性，而且可以以很低的预测误差预测稳定度，为紧急控制提供了更多的时间和指导。
Data-drivenapproachesusingsynchronousphasormeasurementsareplayinganimportant roleintransientstabilityassessment(TSA).Forpost-disturbanceTSA,thereisnotadefiniteconclusion about how long the response time should be. Furthermore, previous studies seldom considered the confidence level of prediction results and specific stability degree. Since transient stability can develop very fast and cause tremendous economic losses, there is an urgent need for faster response speed, credible accurate prediction results, and specific stability degree. This paper proposed a hierarchical self-adaptive method using an integrated convolutional neural network (CNN)-based ensemble classifier to solve these problems. Firstly, a set of classifiers are sequentially organized at differentresponsetimestoconstructdifferentlayersoftheproposedmethod. Secondly,theconfidence integrated decision-making rules are defined. Those predicted as credible stable/unstable cases are sent into the stable/unstable regression model which is built at the corresponding decision time. The simulation results show that the proposed method can not only balance the accuracy and rapidity of the transient stability prediction, but also predict the stability degree with very low prediction errors, allowing more time and an instructive guide for emergency controls..