Arc Fault Detection in DC Distribution Using Semi-Supervised Ensemble Machine Learning
| Autor: | Vu Le, Chad Miller, Tsao-Bang Hung, Xiu Yao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 05 social sciences Arc-fault circuit interrupter 020207 software engineering Pattern recognition 02 engineering and technology Ensemble learning Fault detection and isolation Electric arc Support vector machine Microcontroller 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Artificial intelligence business Performance metric 050107 human factors Randomness |
| Zdroj: | 2019 IEEE Energy Conversion Congress and Exposition (ECCE). |
| DOI: | 10.1109/ecce.2019.8913286 |
| Popis: | Series arc fault detection in a dc system is a challenging task due to the randomness of arc discharge and the dynamic behavior dependence on the system current level. DC arc faults could potentially create a fire hazard if not detected and isolated quickly. This paper introduces two ways to improve the predictive performance of existing conventional machine learning (ML) algorithms as an arc fault detection method. A semi-supervised ML ensures sufficient training data when there is a vast number of unlabeled data and limited labeled data. An ensemble ML method further superimposes on a conventional ML algorithm to create a better classifier, which reduces the bias and decision variance. The goal is to evaluate the effectiveness of both methods in dc arc fault detection. Accuracy, precision, and recall scores are used as the key performance metrics. Finally, an experimental arc fault detection was conducted using a microcontroller Udoo X86 with detection latency time as a performance metric. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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