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In this study, an application of support vector machine (SVM) for early fault detection in increasing the level of the start-up vessel in a Benson type once-through boiler during load changes is presented. The level increasing in the start-up vessel is happened due to thermal conditions disruption inside the boiler especially while the unit load is ramped-down. In this regard, first, the variables effective on increasing the level of start-up vessel was identified based on experimental data from a power plant unit, then the dimension of input variables was reduced by selecting appropriate features. Experimental results show that the hotwell surfaces’ temperature could be considered as the most appropriate indicator for steam quality deterioration. By comparing the extracted features from healthy and unhealthy conditions, appropriate fault model was developed using SVM with radial basis function (RBF) as the kernel. The performances of fault detection system were evaluated with respect to the similar faults at two different time periods happen in a steam power plant. The obtained results show the accuracy and feasibility of the proposed approach in early detection of faults during the unit’s load variations. Advantages of the proposed technique is preventing false alarm in power plants’ boilers as load changes.

Keywords: Fault detection, Feature Extraction, Power plant unit, Support vector machine, Transient condition

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