智能风电机组叶片故障监测系统设计与实现  

Design and Implementation of Blade Fault Monitoring System for Smart Wind Turbine

作　　者：董礼 苏宝定 张振宇 张炤 刘方涛 DONG Li;SU Baoding;ZHANG Zhenyu;ZHANG Zhao;LIU Fangtao(CGN Wind Energy Limited,Beijing 100070,China;Beijing New3s Technology Pty Ltd.,Beijing 100089,China)

机构地区：[1]中广核风电有限公司,北京100070 [2]北京星闪世图科技有限公司,北京100089

出　　处：《电工技术》2021年第20期75-78,91,共5页Electric Engineering

摘　　要：随着风机电力设备的不断增加,采用人工管理方式将无法有效地对众多风机电力设备缺陷进行高效的维护和管理,不仅影响电力设备缺陷管理效率,还可能因工作失误而导致设备漏检。为此,采用JAVA技术及B/S三层架构进行风电机组叶片裂纹监测与智能识别系统的设计与应用。其中,前端采用VUE框架开发实现,业务逻辑处理层采用SPRING BOOT框架设计,数据库采用MySQL关系型数据库系统进行系统内相关数据存储与管理,智能识别缺陷采用计算机视觉YOLOv4算法。试验证明,该系统可提高国网电力设备缺陷识别和分析效率。With the continuous increase of wind turbine power equipment,the manual management method will not be able to effectively maintain and manage the defects of many wind turbine power equipment.Using manual management not only affects the efficiency of power equipment defect management,but also may lead to equipment missing inspection due to work errors.To this end,JAVA technology and B/S three-tier architecture are used to design and apply wind turbine blade crack monitoring and intelligent identification systems.Among them,the front end is developed and implemented using the VUE framework,the business logic processing layer is designed with the SPRING BOOT framework,the database uses the MySQL relational database system for storage and management of related data in the system,and the intelligent identification of defects uses the computer vision YOLOv4 algorithm.Experiments show that the efficiency of defect identification and analysis of power equipment in State Grid is improved.

关 键 词：台账管理 叶片故障监测 叶片巡检 智能检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]