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Noise invariant partial discharge classification based on convolutional neural network
- Source :
- Measurement. 177:109220
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Partial discharge (PD) pattern recognition is essential since it can help to identify the nature of the insulation defect. Numerous machine learning models have been utilized for PD classification applications in the past. However, traditional machine learning models rely on manual feature extraction to obtain training data. They are usually trained using clean PD data measured in the laboratory but are expected to work on-site where some degree of interference or noise is expected. When tested using clean PD data, most machine learning models can easily achieve above 90% accuracy. However, when tested using PD data overlapped with noise, classification accuracy reduces significantly. In this work, the development of a convolutional neural network (CNN)-based PD classification system using transfer learning was proposed. In order to achieve a more practical performance evaluation, a modified 10-fold cross-validation procedure was used where the CNN-based PD classifier was trained using clean PD data but tested using PD data that has been overlapped by noise. The results showed that CNN-based PD classifier was able to achieve up to 16.90% higher classification accuracy under noise contamination compared to traditional machine learning with manual feature extraction. This shows that the proposed method was able to retain higher classification accuracy in the presence of noise.
- Subjects :
- business.industry
Computer science
Applied Mathematics
020208 electrical & electronic engineering
010401 analytical chemistry
Feature extraction
Pattern recognition
02 engineering and technology
Condensed Matter Physics
01 natural sciences
Convolutional neural network
0104 chemical sciences
Noise
Interference (communication)
Pattern recognition (psychology)
Classifier (linguistics)
Partial discharge
0202 electrical engineering, electronic engineering, information engineering
Artificial intelligence
Electrical and Electronic Engineering
Transfer of learning
business
Instrumentation
Subjects
Details
- ISSN :
- 02632241
- Volume :
- 177
- Database :
- OpenAIRE
- Journal :
- Measurement
- Accession number :
- edsair.doi...........4f6fe813bc978305ca02ace503943f28