Image-Based Incipient Fault Classification of Electrical Substation Equipment by Transfer Learning of Deep Convolutional Neural Network

A transfer learning-based deep convolutional neural network (DCNN) is employed in this article to identify several typical electrical substation equipment incipient faults. Image dataset contains 11 000 captured electrical substation equipment images, and each image inside the dataset was labeled as...

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Bibliographic Details
Published in:Canadian journal of electrical and computer engineering 2022, Vol.45 (1), p.1-8
Main Authors: Guan, Xiangyu, Gao, Wei, Peng, Hui, Shu, Naiqiu, Gao, David Wenzhong
Format: Article
Language:English
Subjects:
Artificial neural networks
Circuit faults
Computer architecture
Convolutional neural network (CNN)
Convolutional neural networks
Datasets
Electrical equipment
fault classification
Fault detection
Fault diagnosis
Feature extraction
Image classification
Insulation
Learning curves
Neural networks
Substations
Training
transfer learning
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Summary:A transfer learning-based deep convolutional neural network (DCNN) is employed in this article to identify several typical electrical substation equipment incipient faults. Image dataset contains 11 000 captured electrical substation equipment images, and each image inside the dataset was labeled as normal condition or typical incipient faults (insulation oil leakage, insulator contamination, rusting, and paint off). High-dimension features of electrical substation equipment images were first extracted by classical pre-trained DCNN architectures through the transfer learning method, and different incipient faults were then classified by the fully-connected (FC) neural network using the SoftMax activation function. Remarkable fault classification accuracy was obtained on the validation image, which verifies the effectiveness of the purposed method. Performances of various pre-trained classic DCNN architectures were explored and evaluated by t-distributed stochastic neighbor embedding (t-SNE) feature cluster maps, learning curves, and confusion matrixes. Results show that a model consists of MobileNetV2 DCNN architecture and two FC neural network layers could finalize fault classification tasks on 1000 images in 25 s with an accuracy of 98% which achieves better performance than traditional image classify methods. The proposed method could be useful for designing an electrical substation image monitoring system thus providing early prediction of incipient equipment faults.
ISSN:2694-1783
0840-8688
2694-1783
DOI:10.1109/ICJECE.2021.3109293