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Classification of P...
Classification of Partial Discharges Originating from Multi-level PWM Using Machine Learning
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- Balouji, Ebrahim, 1985 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Hammarström, Thomas, 1974 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- McKelvey, Tomas, 1966 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2022
- 2022
- Engelska.
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Ingår i: IEEE Transactions on Dielectrics and Electrical Insulation. - 1558-4135 .- 1070-9878. ; 29:1, s. 287-294
- Relaterad länk:
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- A Partial discharge (PD) is a small, localized breakdown which often appears in high voltage insulation systems. PDs can be initiated within material defects such as voids or cracks when exposed to sufficiently high electric field levels. In this research work, we study and propose a machine learning approach to detect and classify PDs originating from multilevel pulse width modulation (PWM) waveforms as utilized in power electronic devices such as inverters and variable frequency drives. Due to the increased use of power conversion units, new types of higher magnitude PDs have been observed to increase insulation degradation. By creating a cavity placed at different locations within the test object and exposing it to PWM voltage waveforms with two different rise times, a total of 345660 PD events are recorded and organized into 10 different classes. The maximum PD amplitude, duration, the time distance between consecutive PDs, and the area under the PD are used as features for PD classification. A unique way of concatenating a sequence of the extracted features to capture the temporal dependence of consecutive PDs is also presented. It is observed that when creating a sequence of information from consecutive observed PDs, a significant increase in classification accuracy can be obtained. Trained classifiers based on ensemble bagged decision trees (DT) and long short-term memory (LSTM) architecture resulted in 95.3% and 98.5% average classification accuracy on test data respectively.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- Pulse width modulation
- Partial discharges
- High-voltage techniques
- Partial discharges
- Voltage measurement
- Feature extraction
- Deep learning
- Machine learning
- pulse width modulation
- Feature engineering
- Insulation
- Switches
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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