Research on fault information fusion and intelligent processing of wind farm secondary system

In view of the complexity and high failure rate of secondary systems in modern wind farms, this paper discusses a new fault information fusion and intelligent processing strategy. Based on the structure and common fault types of wind farm secondary system, the characteristics and interrelationship of various fault information are described in detail. On this basis, an intelligent fault diagnosis model based on deep learning is constructed. The algorithm uses advanced data fusion technology to extract key fault features and effectively suppress noise interference and redundant information. By training the model to identify and classify fault modes, various fault sources in the secondary system can be accurately located in real time. In addition, this study also explores the relevant methods of fault prediction and health status assessment, aiming to improve the stability and reliability of wind farm secondary systems through comprehensive analysis of historical data and real-time monitoring information. The experimental part shows the application effect of the proposed method in actual wind farm cases, and verifies its superior performance in fault detection accuracy, processing timeliness and decision-making effectiveness, so as to provide a strong technical support for the intelligent operation and maintenance of wind farms. This study not only enriches the theoretical system of intelligent fault handling of wind farm secondary system, but also opens up new ideas for intelligent monitoring and maintenance practice in the future clean energy field.