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Effects of shorter phase-resolved partial discharge duration on PD classification accuracy
- Source :
- International Journal of Power Electronics and Drive Systems (IJPEDS). 11:326
- Publication Year :
- 2020
- Publisher :
- Institute of Advanced Engineering and Science, 2020.
-
Abstract
- Partial discharge (PD) pattern recognition is useful to diagnose insulation condition. PD measurement data is commonly represented in phase-resolved partial discharge (PRPD) format. PRPD is useful as it provides a visible pattern for different PD source and various features can be extracted for PD pattern recognition. Shorter PRPD duration will enable more training data but the information in each data is less and vice versa. This works aims to investigate the effects of using very short duration PRPD data on the accuracy of PD pattern recognition. The results conclude that machine learning models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are robust enough such that reduction of PRPD duration from 15-seconds to 1-second causes less than 5 % drop in the classification accuracy. However, this is only true for noise free condition. When the same PD data is overlapped with random noise, the classification accuracy suffers a significant reduction up to 19%. Therefore, longer PRPD duration is recommended to withstand the effects of noise contamination.
- Subjects :
- Artificial neural network
Computer science
business.industry
Phase (waves)
PRPD
Energy Engineering and Power Technology
Pattern recognition
Reduction (complexity)
Support vector machine
Noise
Duration (music)
Partial discharge
Pattern recognition (psychology)
Artificial intelligence
Electrical and Electronic Engineering
business
Subjects
Details
- ISSN :
- 2722256X and 20888694
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- International Journal of Power Electronics and Drive Systems (IJPEDS)
- Accession number :
- edsair.doi.dedup.....4088f6fa8397c1d8c45e1ec8635eae83