基于递归主成分分析的电厂故障监测

2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2016-11-01 DOI:10.1109/ROPEC.2016.7830609

Alberto Sánchez, Mauricio Redrobán, Omar Aguirre

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引用次数: 0

摘要

以12MW蒸汽双燃料电厂为研究对象，探讨了递归主成分分析法对故障源进行识别和分类的方法。算法评估是利用相关厂宽信息数据离线进行的。提出了一种基于归一化数据的简单贡献矩阵来诊断设备故障。结果表明，可以检测、分类甚至预测工厂故障的来源。

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Power plant fault monitoring using Recursive Principal Component Analysis RPCA

Recursive Principal Components Analysis is explored as a method to identify and classify fault sources in a 12MW steam dual fuel power plant. The algorithm assessment is performed off-line by using data of relevant plant wide-information. A simple contributions matrix based in normalized data is proposed to diagnose plant faults. Results indicate it is possible to detect, classify and possibly even predict sources of plant failure.

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2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

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