基于大数据的多维度水电机组健康评估与诊断

Multi-dimension health assessment and diagnosis of hydropower unit based on big data

考虑到当前水电机组安全高效运行领域存在的健康状态评估不准确、故障样本少、故障知识不健全和大型机组安全高效运行准则缺乏等问题,本文通过分析提炼水电机组试验、在线监测及运行维护等海量健康数据,提出了表征机组运行状态的特征参数,揭示了各特征参数与机组运行工况参数之间的耦合关系,最终建立了基于健康样本的多维度水电机组健康评估和性能退化预测理论方法。此外,本研究还提出了大型机组安全稳定运行分区准则。基于所建模型和准则,建立了大型水电机组远程状态监测与诊断系统平台。为保障水电机组安全、稳定和高效运行提供了重要技术支撑。

At present, there are some problems in the safe and efficient operation of hydropower units,such as inaccurate health assessment, fewer fault samples,inadequate fault knowledge, lack of safe and efficient operation guidelines for large units,etc. The healthy data of hydropower unit test,on-line monitoringand operation and maintenance are analyzed. The characteristic parameters that represent the running statusof the unit are proposed. The coupling relationship between characteristic parameters and the unit operatingcondition parameters is revealed. The multi-dimensional health assessment and performance degradation prediction theory of hydropower unit based on healthy samples is established. The zoning guideline for safeand stable operation of large-scale units is proposed. Based on the established model and criteria, a re-mote hydropower unit monitoring and diagnosis system platform is established. It provides important technical support for ensuring the safe,stable and efficient operation of hydropower unit.