A Multistage Rotor Imbalance Diagnosis Method for Horizontal-Axis Wind Turbines Based on an Expert System and Improved Convolutional Neural Networks

Abstract

Rotor imbalance in wind turbines can lead to significant safety risks and economic losses, which makes accurate fault analysis essential for reliable operation. However, accurate and robust analysis under varying wind conditions remains challenging. To address these issues, a multistage rotor imbalance analysis framework is proposed, which includes fault detection, fault type classification, fault severity classification and blade localisation. First, a knowledge-based expert system with a multinode decision structure is developed, where nacelle vibration is used for rotor status detection and output power is used for imbalance type classification. Second, a convolutional neural network with training interference that incorporates variable dropout and microbatch training strategies is introduced to improve noise tolerance. Moreover, an adaptive deep interpolation convolutional neural network (DI-DCNN) is proposed for handling wind-induced variations through a multisubnetwork weighting and fusion mechanism. Third, the harmonic components of the nacelle vibration are compensated for using information on the rotor azimuth to achieve blade localisation. Experimental results under various wind conditions demonstrate that the proposed method achieves high accuracy across all the tasks and maintains stable performance under varying turbulence intensities. Moreover, the framework does not require additional sensors and has good generalisation ability. The proposed method provides a unified and robust solution for rotor imbalance detection, classification, and localisation in wind turbines.