Leakage Detection of Steam Boiler Tube in Thermal Power Plant Using Principal Component Analysis
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Abstract
Tube leakage of steam boiler can decrease the whole efficiency of power plant cycle, and eventually cause an unscheduled shutdown. In this paper, we propose a leakage detection method for steam boiler tubes in thermal power plant (TPP) using principal component analysis and exponentially weighted moving average (EWMA). To determine the number of principal components, the cumulative percent variance technique is employed, and the Q statistic is used as the detection index. If the Q statistic of an unseen sample is larger than a predefined threshold value, the sample is detected as a fault sample and an alarm signal is generated. EWMA is used to reduce false alarms. To demonstrate the performance, we apply the proposed method to an unplanned shutdown case due to boiler tube leakage, which is collected from distributed control systems of 250 MW coal-fired TPP. The experiment results show that the proposed method can detect failure symptoms of the case successfully.
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