摘要
总结了风电机组及风电场常见的故障类型以及故障分析理论,并针对风电机组故障诊断系统的分散性,提出风电机组故障诊断技术今后的发展方向,即对现有单一风场分散的监控与管理系统进行整合,通过异构数据的采集、存储及管理技术,建立统一完整的实时和历史数据库。基于完整的风电场统管数据库,开发风电机组各个重要子系统的在线监测系统,进而整合优化,实现基于数据融合方法的具有自学习能力的风电场智能专家故障预测。
The common fauh types of wind turbines and wind farms and fault analysis theory are summed up, and in light of the dispersion of the wind turbines＇ fault diagnosis system, the future development direction of wind turbines＇ fault diagnosis technology was put forward. That is to integrate monitoring and management system in which the existing single wind farms distribute sparsely and establish a unified complete real -time and history database through heterogeneous data collection, storage and management technology. Based on the complete management database of wind farms, on - line monitoring system for wind turbines＇ key subsystem is developed, and then integrated and optimized. It is to achieve fault prediction of wind farm intelligent expert with self - learning ability based on data fusion method.
出处
《黑龙江电力》
CAS
2017年第2期173-177,共5页
Heilongjiang Electric Power
基金
国家科技支撑计划项目(2015BAA06B02)
关键词
风力发电
齿轮箱
故障预测
故障诊断
智能化
wind power generation
gearbox
fault prediction
fault diagnosis
intelligence
