Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF
| Autor: | Huawei Wu, Jiabiao Yi, Zimin Wang, Mingzhu Tang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
education.field_of_study
Computer science Chemical technology Population TP1-1185 Fault (power engineering) Biochemistry Turbine Article fault detection Atomic and Molecular Physics and Optics Fault detection and isolation Analytical Chemistry wind turbine generator set electric pitch system Local optimum Control theory Test set Electrical and Electronic Engineering Fault model extreme random forest education Instrumentation grey wolf optimization Generator (mathematics) |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 21 Issue 18 Sensors, Vol 21, Iss 6215, p 6215 (2021) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21186215 |
| Popis: | It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set. |
| Databáze: | OpenAIRE |
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