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Fault-cause identification method based on adaptive deep belief network and time–frequency characteristics of travelling wave

机译：基于自适应深度置信网络和行波时频特性的故障原因识别方法

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摘要

Accurate fault-cause identification is highly important to the fault analysis of overhead transmission lines (OTLs). In order to improve the efficiency and accuracy of fault identification, this study proposes a fault identification method based on the ADBN (adaptive deep belief network) model and the time-frequency characteristics of a travelling wave. According to the mechanisms of different OTL faults, the appropriate time-frequency characteristic parameters of the fault current travelling wave were selected as the input of the ADBN model, and the fault-type labels were selected as the output. The ADBN model introduces the idea of adaptive learning rate into CD (contrastive divergence) algorithm and improves its performance with self-adjusting learning rate. The parameters of the ADBN model were pre-trained with the improved CD algorithm and adjusted by back propagation algorithm with the labels of the samples. The performance of the ADBN model was verified by field data, and the accuracy of fault identification was analysed under different model parameters, characteristic parameters, and sample sizes. The results showed that the model helps to characterise the inherent relationship between characteristic parameters and fault causes, and the proposed method can effectively identify different fault causes in OTLs.
机译：准确的故障原因识别对于架空传输线（OTL）的故障分析非常重要。为了提高故障识别的效率和准确性，本文提出了一种基于ADBN（自适应深度信念网络）模型和行波时频特性的故障识别方法。根据不同的OTL故障的机理，选择故障电流行波的时频特征参数作为ADBN模型的输入，并选择故障类型标签作为输出。 ADBN模型将自适应学习率的概念引入CD（对比度发散）算法中，并通过自调整学习率来提高其性能。使用改进的CD算法对ADBN模型的参数进行预训练，并通过反向传播算法对样本的标签进行调整。通过现场数据验证了ADBN模型的性能，并分析了在不同模型参数，特征参数和样本量下故障识别的准确性。结果表明，该模型有助于表征特征参数与故障原因之间的内在联系，所提方法可以有效地识别OTL中不同的故障原因。

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