Fault diagnosis of wind power gearbox based on IPSO-FNN
| Autor: | Rui-fei Bai, Wei Zhang, Wei-min Huang, Ding-hai Rui |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Wind power
Artificial neural network business.industry Computer Science::Neural and Evolutionary Computation 010401 analytical chemistry Particle swarm optimization 02 engineering and technology 021001 nanoscience & nanotechnology Fault (power engineering) 01 natural sciences 0104 chemical sciences Control theory Differential evolution Component (UML) Convergence (routing) Entropy (information theory) 0210 nano-technology business |
| Zdroj: | 2020 Chinese Automation Congress (CAC). |
| DOI: | 10.1109/cac51589.2020.9326534 |
| Popis: | Wind power gearbox (WPG) is an important component of wind turbines, which is prone to faults due to factors such as working environment and complex structure. Furthermore, the causality of faults is difficult to be expressed by the mathematical model. A fault diagnosis method of WPG is proposed based on the improved particle swarm optimization combined with fuzzy neural network (IPSO-FNN). In order to improve the efficiency of network learning algorithm, IPSO is used to learn network parameters. Fitness variance is introduced to characterize the state of particles, and differential evolution is applied to premature particles to improve the diversity of particle swarm. Simulation results show that the proposed method has higher diagnostic accuracy and faster convergence speed than FNN and PSO-FNN methods. |
| Databáze: | OpenAIRE |
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