Abstract
In this paper, an evolved FCM-based clustering method combined with entropy theory is proposed to develop a working condition classification model for the furnace system in coal-fired power plants. To overcome the disadvantage in beforehand determination of clustering number in basic FCM method, Silhouette index is selected as a parameter to evaluate clustering number adaptively in the process. Each time the FCM runs, the selected Silhouette index evaluates the clustering results considering both close and separation degree. Six datasets from UCI machine learning repository are used to certify the effectiveness of the evolved FCM method. Furthermore, pressure sequences from a 300-MW boiler are then discussed as the industrial case study. Three kinds of entropy values, featured from pressure sequence in time–frequency domain, are obtained for further clustering analysis. The clustering results show the strong relationship between boiler’s load and pressure sequences in furnace system. This method can be considered a reference method for data mining in other fluctuating and time-varying sequences.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
