A Feature Selection Method for High Impedance Fault Detection
| Autor: | Khalil El-Arroudi, Qiushi Cui, Yang Weng |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Signal Processing (eess.SP)
Computer science 020209 energy Feature extraction Energy Engineering and Power Technology Feature selection 02 engineering and technology Systems and Control (eess.SY) Fault (power engineering) computer.software_genre Power (physics) High impedance Ranking Feature (computer vision) 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering Computer Science - Systems and Control Dependability Data mining Electrical Engineering and Systems Science - Signal Processing Electrical and Electronic Engineering computer |
| DOI: | 10.48550/arxiv.1808.04454 |
| Popis: | High impedance fault (HIF) has been a challenging task to detect in distribution networks. On one hand, although several types of HIF models are available for HIF study, they are still not exhibiting satisfactory fault waveforms. On the other hand, utilizing historical data has been a trend recently for using machine learning methods to improve HIF detection. Nonetheless, most proposed methodologies address the HIF issue starting with investigating a limited group of features and can hardly provide a practical and implementable solution. This paper, however, proposes a systematic design of feature extraction, based on an HIF detection and classification method. For example, features are extracted according to when, how long, and what magnitude the fault events create. Complementary power expert information is also integrated into the feature pools. Subsequently, we propose a ranking procedure in the feature pool for balancing the information gain and the complexity to avoid over-fitting. For implementing the framework, we create an HIF detection logic from a practical perspective. Numerical methods show the proposed HIF detector has very high dependability and security performance under multiple fault scenarios comparing with other traditional methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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