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Independent Component Analysis–Based Fuel Type Identification for Coal-Fired Power Plants

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Independent component analysis (ICA) and support vector machine (SVM) techniques were used to identify the fuel types. Flame oscillation signals were captured by a flame monitor. Thirty flame features were extracted from each flame oscillation signal to form an original feature vector. The ICA technique was applied to choose the independent flame features from each original feature vector. An SVM model was deployed to map the flame features to an individual type of fuel. The results obtained by using eight different types of coal demonstrated that the ICA technique combining with a well trained SVM can be used for identifying the fuel types, and the average success rate was 96.2% in 20 trials. The ICA preceded by principal component analysis (PCA) used for whitening and dimension-reducing performed a bit better than individually using the ICA technique, and the average success rate of fuel type identification was 97.8% in 20 trials.
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Keywords: Feature extraction; Flame feature; Fuel type; Independent component analysis (ICA); Support vector machine (SVM)

Document Type: Research Article

Affiliations: 1: Key Laboratory of Precision Opto-mechatronics Technology of Ministry of Education,School of Instrument Science and Opto-Electronic Engineering, Beihang University, Beijing, China 2: School of Chemistry and Environment,Beihang University, Beijing, China 3: School of Engineering and Digital Arts,University of Kent, Canterbury,Kent, UK

Publication date: March 1, 2012

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