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.

中文翻译：

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用于复杂电能质量扰动分类的三层贝叶斯网络


本文提出了一种使用三级多重连接贝叶斯网络来检测和分类复杂电能质量扰动（PQD）的新分类方法。首先，建立由特征证据层、干扰状态层和环境证据层组成的模型，分别表示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的环境因素。其次，根据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，通过计算给定观察到的证据的每个事件的后验边际概率来确定分类。新方法不仅利用了现有的特征提取方法，还考虑了历史数据和其他周围因素。仿真结果和实际PQ信号测试表明，TLBN对复杂扰动的分类性能优于现有文献中的其他方法。