摘要
Abstract
In the operation of the electrical DCS system in thermal power plants,the fault attributes are diverse,and the drawbacks of the single threshold fault detection mode are obvious.Therefore,it designs a fault diagnosis method for the electrical system of a thermal power plant based on multi parameter integration.Taking the electrical system of a certain thermal power plant as an example,by adopting modern signal processing and artificial intelligence technology fault diagnosis methods,it can achieve intelligent monitoring of the electrical system.By establishing multiple analog signal input acquisition channels,it achieves integrated monitoring of multiple parameters in electrical systems.It adopts intelligent data processing technology to standardize the collected multiple analog signals,unifies the data format,and stores it in the terminal database.It introducs adaptive learning rates or centroid weights to achieve adaptive clustering centroid updates,thereby improving the K-means clustering algorithm.Based on this,it selects sensitive feature parameters for fault diagnosis in electrical systems.By adopting the method,not only can the fault phenomena during the operation of the electrical system be accurately diagnosed,but also timely warnings can be issued and the types of faults can be accurately identified,enhancing the safety and maintenance efficiency of the system.关键词
热电厂/多参数/K-means 聚类/故障诊断/智能监控Key words
thermal power plant/multi parameter/K-means clustering/fault diagnosis/intelligent monitoring分类
信息技术与安全科学
