A Decision-Dependent Stochastic Approach for Joint Operation and Maintenance of Overhead Transmission Lines After Sandstorms

Yin, Wenjun; Feng, Shuanglei; Hou, Jiazuo; Qian, Chengchen; Hou, Yunhe

doi:10.1109/jsyst.2022.3185072

Cited by 4 publications

(2 citation statements)

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“…The characteristics of the three datasets are shown in Table 1. 1 We have collected data on three types of UHV line faults, 110 KV, 330 KV and 750 KV. Each type contains multiple UHV lines from different regions.…”

Section: Data Setsmentioning

confidence: 99%

“…Overhead transmission lines [1], which transmit power by means of cables and wires suspended from supports, are mainly used for long-distance power transmission to transmit power generated by power plants to load centers, cities or other power-demanding areas. Due to their exposure to the natural environment, overhead transmission lines are susceptible to external factors such as inclement weather, strong winds, and lightning strikes, and thus have a high probability of line failures.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐view synergistic enhanced fault recording data for transmission line fault classification

Jia,

Huang,

Han

et al. 2024

IET Communications

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Fault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method.

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Section: Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%