Xinzhu Lv
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xinzhu Lv has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Ao Liu
Yanzhao Electric Power Laboratory, North China Electric Power University, Baoding 071003, China
1 shared publication - Jiangbo Qian
North China Electric Power University, Baoding, Hebei Province 071003, People’s Republic of China
1 shared publication - Liwei Shao
School of Automation, Beijing Institute of Technology, Beijing 100081, China
1 shared publication - Shangyun Yao
China National Water Resources & Electric Power Materials & Equipment Group Co., Ltd., Beijing 100040, China
1 shared publication - Zhangzhuo Sun
Yanzhao Electric Power Laboratory, North China Electric Power University, Baoding 071003, China
1 shared publication
Latest Publications
- Open Access
Icing Detection of Wind Turbine Blades Based on an Improved PP-YOLOE Detection Network
In cold and highly humid regions, wind turbine blades (WTB) are susceptible to icing, which can have a significant impact on the security and economic operation of turbines. Therefore, precise and prompt icing status detection is pivotal for maintaining wind turbine operational normalcy. In this research, an improved PP-YOLOE network is developed for classifying and detecting the icing state of WTB. First, a dataset of WTB icing is constructed based on a wind tunnel laboratory and expanded to improve the generalization of the model. To enhance feature representation, the network architecture was improved by embedding a coordinate attention (CA) mechanism and integrating atrous spatial pyramid pooling (ASPP) to better capture multi-scale contextual information. Moreover, a key innovation of this work is the novel application of a particle swarm optimization (PSO) algorithm to systematically automate hyperparameter tuning. Through ablation experiments and comparative tests, the improved PP-YOLOE network demonstrates superior overall performance on this dataset, achieving a multiple average precision of 0.94. It surpasses the original model across multiple evaluation metrics, indicating a robust and meaningful enhancement. The improved PP-YOLOE network proposed in this study provides a promising and effective method for WTB icing detection. This work provides a paradigm for applying advanced deep learning techniques to enhance intelligent industrial inspection tasks.
Open AccessIcing Detection of Wind Turbine Blades Based on an Improved PP-YOLOE Detection Network
In cold and highly humid regions, wind turbine blades (WTB) are susceptible to icing, which can have a significant impact on the security and economic operation of turbines. Therefore, precise and prompt icing status detection is pivotal for maintaining wind turbine operational normalcy. In this research, an improved PP-YOLOE network is developed for classifying and detecting the icing state of WTB. First, a dataset of WTB icing is constructed based on a wind tunnel laboratory and expanded to improve the generalization of the model. To enhance feature representation, the network architecture was improved by embedding a coordinate attention (CA) mechanism and integrating atrous spatial pyramid pooling (ASPP) to better capture multi-scale contextual information. Moreover, a key innovation of this work is the novel application of a particle swarm optimization (PSO) algorithm to systematically automate hyperparameter tuning. Through ablation experiments and comparative tests, the improved PP-YOLOE network demonstrates superior overall performance on this dataset, achieving a multiple average precision of 0.94. It surpasses the original model across multiple evaluation metrics, indicating a robust and meaningful enhancement. The improved PP-YOLOE network proposed in this study provides a promising and effective method for WTB icing detection. This work provides a paradigm for applying advanced deep learning techniques to enhance intelligent industrial inspection tasks.