Detection and Classification of Transmission Line Faults Based on Unsupervised Feature Learning and Convolutional Sparse Autoencoder
Abstract
We present in this paper a novel method for fault detection and classification in power transmission lines based on convolutional sparse autoencoder. Contrary to conventional methods, the proposed method automatically learns features from a dataset of voltage and current signals, on the basis of which a framework for fault detection and classification is created. Convolutional feature mapping and mean pooling are implemented in order to generate feature vectors with local translation-invariance for half-cycle multi-channel signal segments. Fault detection and classification are achieved by a softmax classifier using the feature vectors. Further, the proposed method is tested under different sampling frequencies and signal types. The generalizability of the proposed method is also verified by adding noise and measurement errors to the data. Results show that the proposed method is fast and accurate in detecting and classifying faults, and is practical for online transmission line protection for its high robustness and generalizability.
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A convolutional sparse autoencoder-based unsupervised learning method has been proposed for power transmission line fault diagnosis based on voltage and current signals, and it can detect faults within 7 ms latency.
“For example, a convolutional sparse autoencoder-based unsupervised learning method has been proposed for power transmission line fault diagnosis based on voltage and current signals [250]. It can detect faults within 7 ms latency, which is appealing to real-world applications.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [250]
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- Authors
- Chen, K., Hu, J., & He, J.
- Affiliations
- State Key Laboratory of Power System Operation and Control (CN)
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2016
- Pages
- 1-1
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2513676771
- Cited by
- 176
- References
- 46
- Citations by year
- 2026: 2 · 2025: 18 · 2024: 22 · 2023: 31 · 2022: 23 · 2021: 27 · 2020: 25 · 2019: 17 · 2018: 8 · 2017: 3
- FWCI
- 7.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Smart Grid (publishedVersion)
- Funders
- Ministry of Science and Technology of the Peoples Republic of China (2013CB228206)
- National Natural Science Foundation of China (51429701)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Power Systems Fault Detection
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Keywords
- transmission line fault detection, fault classification, unsupervised feature learning, power system protection, voltage and current signals, online protection
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