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

机译：基于自适应深度信仰网络的故障原因识别方法和旅行波的时频特性

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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开销传输线（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，本研究提出了一种基于ADBN（自适应深度信念网络）模型的故障识别方法和行驶波的时频特性。根据不同OTL故障的机制，选择了故障电流行驶波的适当时频特性参数作为ADBN模型的输入，选择故障型标签作为输出。 ADBN模型将自适应学习率的思想介绍为CD（对比分歧）算法，并通过自调节学习率提高其性能。 ADBN模型的参数用改进的CD算法预先训练，并通过对样本标签进行后传播算法调整。 ADBN模型的性能由现场数据验证，在不同的模型参数，特征参数和样本尺寸下分析了故障识别的准确性。结果表明，该模型有助于表征特征参数和故障原因之间的固有关系，并且所提出的方法可以有效地识别OTL中的不同故障原因。

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