Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning
| Autor: | Surya Teja Kandukuri, Huynh Van Khang, Kjell G. Robbsersmyr |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Bearing (mechanical) Stator Computer science Rotor (electric) 02 engineering and technology Fault (power engineering) Autoencoder Turbine Fault detection and isolation law.invention 020901 industrial engineering & automation law Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Induction motor |
| Zdroj: | 2018 21st International Conference on Electrical Machines and Systems (ICEMS). |
| DOI: | 10.23919/icems.2018.8549293 |
| Popis: | Pitch systems are among the wind turbine components with most frequent failures. This article presents a multicomponent fault detection for induction motors and planetary gearboxes of the electric pitch drives using only the three-phase motor line currents. A deep autoencoder is used to extract features from the extended Park's vector modulus of the motor three-phase currents and a support vector machine to classify faults. The methodology is validated in a laboratory setup of a scaled pitch drive, with four commonly occurring faults, namely, the motor stator turns fault, broken rotor bars fault, planetary gearbox bearing fault and planet gear faults, under varying load and speed conditions. |
| Databáze: | OpenAIRE |
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