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首页> 外文期刊>Electric power systems research >Unsupervised feature learning for online voltage stability evaluation and monitoring based on variational autoencoder
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Unsupervised feature learning for online voltage stability evaluation and monitoring based on variational autoencoder

机译：基于变化自动化器的在线电压稳定性评估和监控无监督特征学习

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摘要

With the increase of uncertain elements in power systems and extensive deployment of online monitoring devices, it is necessary to search a more real-time and robust voltage stability assessment method. This study, using PMU monitoring data, explores a novel data-driven approach for long-term voltage stability assessment based on variational autoencoder (VAE). Our method is capable of extracting the most representative features by an unsupervised data mining method in a probabilistic learning way. Different from most of familiar feature extraction methods, it regularizes latent features in an expected stochastic distribution. Furthermore, a statistical indicator by sampling latent features after variance reduction is proposed to assess long-term voltage stability. Our approach is tested in various simulated power systems with different load increment models. Other cases show the accuracy and speed of our approach for estimating voltage collapse point. These testing cases successfully demonstrate the accuracy and effectiveness of our approach.
机译：随着电力系统中不确定元素的增加和在线监控设备的广泛部署，有必要搜索更实时和鲁棒的电压稳定性评估方法。本研究采用PMU监测数据探讨了基于变分AutiaceCoder（VAE）的长期电压稳定性评估的新型数据驱动方法。我们的方法能够以概率的学习方式通过无监督的数据挖掘方法提取最具代表性的特征。与大多数熟悉的特征提取方法不同，它在预期的随机分布中规范潜在特征。此外，提出了通过对变差减少进行潜在特征的统计指标来评估长期电压稳定性。我们的方法在具有不同负载增量模型的各种模拟电力系统中进行测试。其他案例显示了我们估算电压折叠点的方法的准确性和速度。这些测试案例成功展示了我们方法的准确性和有效性。

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  • 来源
    《Electric power systems research》 |2020年第5期|106253.1-106253.11|共11页
  • 作者

    Yang Haosen; Qiu Robert C.; Shi Xin; He Xing;

  • 作者单位

    Shanghai Jiao Tong Univ Res Ctr Big Data Engn Technol State Energy Smart Grid Resarch & Dev Ctr Dept Elect Engn Shanghai 200240 Peoples R China;

    Shanghai Jiao Tong Univ Res Ctr Big Data Engn Technol State Energy Smart Grid Resarch & Dev Ctr Dept Elect Engn Shanghai 200240 Peoples R China;

    Shanghai Jiao Tong Univ Res Ctr Big Data Engn Technol State Energy Smart Grid Resarch & Dev Ctr Dept Elect Engn Shanghai 200240 Peoples R China;

    Shanghai Jiao Tong Univ Res Ctr Big Data Engn Technol State Energy Smart Grid Resarch & Dev Ctr Dept Elect Engn Shanghai 200240 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Voltage stability; Variational autoencoder; Unsupervised feature learning; PMU monitoring data;

    机译：电压稳定性;变形式自动化器;无监督的特征学习;PMU监控数据;

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