Low temperature life prediction model of wind turbine stator insulation based on PLS-DBO-RBF

To address the issue of low prediction accuracy in neural networks due to limited data for forecasting the low-temperature lifespan of wind turbine stators, this study proposes a multi-optimization model based on Partial Least Squares-Dung Beetle Optimizer-Radial Basis Function (PLS-DBO-RBF) neural network. PLS is used for dimensionality reduction and feature extraction to address overfitting issues in small sample data; RBFNN serves as a nonlinear mapping tool, with its network parameters globally optimized by the DBO algorithm, thereby enhancing model generalization ability. The experiment collected stator insulation aging data after four groups of low-temperature treatments with different cycles, with 12 samples in each group. The monitoring indicators include breakdown voltage, dielectric loss, etc. Under the same training set and test set partitioning, the performance differences between PLS-DBO-RBF and two single models (RBF optimized only by DBO, RBF reduced dimensionally only by PLS) were compared. The results show that the comprehensive optimization method has better accuracy than the single optimization method in predicting stator insulation life under low-temperature conditions, which is confirmed by multiple evaluation indicators.