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Three-Layer Bayesian Network for Classification of Complex Power Quality Disturbances

机译：复杂电能质量扰动分类的三层贝叶斯网络

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摘要

In this paper, a new classification approach for detection and classification of complex power quality disturbances (PQDs) using a three-level multiply connected Bayesian network is proposed. First, the model consisting of features evidence layer, disturbances state layer, and circumstance evidence layer is established, which represent the features extracted from the sample signal, the state of each single label of PQDs and the circumstance factors that may affect the PQDs, respectively. Second, the parameters of three-level multiply connected Bayesian network (TLBN) are studied from statistical data and Monte Carlo simulations. Finally, the classification is determined by computing the posterior marginal probabilities of each event given observed evidences. The new method not only utilizes the existing features extracting methods, but also takes the historical data, and other surrounding factors into account. Simulation results and real-life PQ signal tests show that the performances of TLBN classification of complex disturbances are better than the other approaches in existing literatures.
机译：本文提出了一种新的分类方法，该方法使用三级多重连接贝叶斯网络对复杂电能质量扰动（PQD）进行检测和分类。首先，建立由特征证据层，扰动状态层和情况证据层组成的模型，分别代表从样本信号中提取的特征，PQD的每个单个标签的状态以及可能影响PQD的情况因素。 。其次，从统计数据和蒙特卡洛模拟研究了三级多重连接贝叶斯网络（TLBN）的参数。最后，在给定观察到的证据的情况下，通过计算每个事件的后缘概率来确定分类。新方法不仅利用现有特征提取方法，而且还考虑了历史数据和其他周围因素。仿真结果和现实生活中的PQ信号测试表明，复杂扰动的TLBN分类性能优于现有文献中的其他方法。

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  • 来源
    《Industrial Informatics, IEEE Transactions on》 |2018年第9期|3997-4006|共10页
  • 作者

    Yi Luo; Kaicheng Li; Yuanzheng Li; Delong Cai; Chen Zhao; Qingxu Meng;

  • 作者单位

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China;

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China;

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China;

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China;

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China;

    State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Feature extraction; Training; Bayes methods; Power quality; Electromagnetics; Neural networks; Power system stability;

    机译：特征提取;训练;贝叶斯方法;电能质量;电磁学;神经网络;电力系统稳定性;

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