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TransLine: transfer learning for accurate and explainable power line anomaly detection with insufficient data

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Abstract

Accurate and automatic power line anomaly detection is critical to the smart grid. However, effective solutions are yet available due to the insufficiency of anomaly data. In this paper, we first collect a dataset from various sources consisting of both normal and abnormal power line images. With this dataset, anomaly detection becomes feasible though with limited accuracy due to the limited size of the dataset. As such, we propose TransLine, an approach based on transfer learning to apply the existing knowledge extracted from large-scale datasets to complement the data insufficiency of power line anomaly detection. TransLine customizes and optimizes the knowledge to automate the power line anomaly detection with high accuracy. The experiment results show that TransLine can achieve an average accuracy of \(96.1\%\) and up to \(98.1\%\) accuracy given only a hundred abnormal images for model training. TransLine also incorporates an explainability module to explain the detection results and enhance its understandability, trustworthiness, and practicalness. TransLine can be a key enabler of the smart grid for great stability and efficiency and can inspire other industrial applications facing data insufficiency issues.

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Notes

  1. In this paper, detection accuracy, or accuracy for short, is equivalent to the accuracy of image classification.

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Acknowledgements

This work was supported in part by the Future Communications Research & Development Programme (FCP) under Grant FCP-SIT-TG-2022-007, Singapore; and in part by the Mitsui Sumitomo Insurance Welfare Foundation Research Grant 2021, RF10021.

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Authors and Affiliations

  1. School of Science and Technology, Singapore University of Social Sciences (SUSS), Singapore, 599494, Singapore

    Fang Liu

  2. Infocomm Technology Cluster, Singapore Institute of Technology (SIT), Singapore, 138683, Singapore

    Wei Zhang, Indriyati Atmosukarto & Teck Wei Low

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  1. Fang Liu
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  2. Wei Zhang
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  3. Indriyati Atmosukarto
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  4. Teck Wei Low
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Correspondence to Wei Zhang.

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This manuscript is extended from our conference paper (Liu et al. 2022).

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Liu, F., Zhang, W., Atmosukarto, I. et al. TransLine: transfer learning for accurate and explainable power line anomaly detection with insufficient data. CCF Trans. Pervasive Comp. Interact. 5, 241–254 (2023). https://doi.org/10.1007/s42486-023-00131-y

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