Intelligent acoustic detection of blade icing on wind turbines: 600 W prototype study

Abstract

Diagnosing wind turbine blade icing is crucial for enhancing the efficiency and reliability of wind power generation in cold regions. Current acoustic-based diagnostic techniques, while cost-efficient, face challenges in precision and signal processing within complex sound environments. For this reason, this paper proposes a new method for diagnosing blade icing, which includes an enhanced deep residual network based on densely connected modules and a data enhancement strategy to improve diagnostic results in complex environments. In particular, blade acoustic signatures, rich in spatial information, are captured using a microphone array. These signals are then processed by a model combining fixed-orientation delay-and-sum beamforming with the enhanced deep residual network. The performance of the proposed method for blade icing damage diagnosis has been evaluated through a 600 W wind turbine under different operating and measurement conditions, and experiments have been conducted under different blade icing positions. The results show that the proposed approach achieved high diagnostic precision, yielding F1-scores of 0.9354 and 0.9297. These scores indicate a substantial improvement in accurately identifying blade icing compared to existing other methods. Furthermore, the competitiveness of the proposed method is further demonstrated through ablation studies. This work makes an important contribution to the sustainable utilization of wind energy resources in cold regions.