Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

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基于自组织神经网络的火电厂健康状态数据提取算法

来源:电工电气发布时间:2019-09-19 10:19 浏览次数:11
基于自组织神经网络的火电厂健康状态数据提取算法
 
吴胜聪,陈雨轩,沈可心,程浩轩
(三峡大学 电气与新能源学院,湖北 宜昌 443002)
 
    摘 要:火电厂设备健康数据提取是火电厂设备状态评估数据处理的一个关键步骤,有利于提高设备状态评估的准确性与效率。将设备状态数据首先利用R 型层次聚类进行特征参数选取与冗余数据清除,再采用自组织神经网络筛选异常值。利用所诉方法对某发电厂的汽泵前置泵设备的监测数据进行健康状态数据提取,发现清除的异常数据远远大于提取出的健康数据,表明该方法清除的数据满足预期,为后续健康状态评估提供了准确的参照数据,并且降低监测数据维度提高评估效率。
    关键词:大数据;自组织神经网络;R 型聚类;电力设备状态数据
    中图分类号:TM621     文献标识码:A      文章编号:1007-3175(2019)09-0027-06
 
Health State Data Extraction Algorithm for Thermal Power Plant Based on Self-Organizing Neural Network
 
WU Sheng-cong, CHEN Yu-xuan, SHEN Ke-xin, CHENG Hao-xuan
(College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)
 
    Abstract: The health data extraction of thermal power plant equipment is a key step in the processing of equipment state assessment of thermal power plants, which is conducive to improving the accuracy and efficiency of equipment state assessment. The power equipment status data were carried out characteristic parameters selection and redundant data eliminating by R-type hierarchical clustering, then the outliers of device status data were filtered by self-organizing neural network. The proposed algorithm was used to extract the health status data from the monitoring data on turbine pump booster pump device in certain power plant. It is found that The clearing abnormal data is far greater than the extracted health data, which indicates that the algorithm meets the expectation. This algorithm provides the accurate reference data for subsequent health assessment, reducing the monitoring data dimension and improving evaluation efficiency.
    Key words: big data; self-organizing neural network; R-type clustering; power equipment status data
 
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