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

© The Institution of Engineering and Technology
Received 10/08/2018, Accepted 02/01/2019, Revised 23/11/2018, Published 15/01/2019

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.

Inspec keywords: power transmission faults; power engineering computing; power overhead lines; backpropagation; belief networks; fault diagnosis; neural nets; time-frequency analysis; feature extraction; fault location

Other keywords: adaptive learning rate; fault analysis; travelling wave characteristics; ADBN model; overhead transmission lines; back propagation algorithm; OTL faults; fault current travelling wave; time–frequency characteristics; self-adjusting learning rate; field data; fault-type labels; adaptive deep belief network; contrastive divergence algorithm; improved CD algorithm; characteristic parameters; fault-cause identification method

Subjects: Mathematical analysis; Overhead power lines; Mathematical analysis; Neural computing techniques; Power engineering computing

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