Abstract
AbstractFlame monitoring and categorization become important in efficient power generation and energy conservation sectors. Flicker Frequency Range (FFR) is considered as the key factor to monitor flames and fuel identification. The fluctuation of combustion arises due to the varying quality of fuel, which causes the overlapping of FFR for different fuels leading to misclassification. In this work, we attempt to classify fuels based on flame characteristics output from a Digital Flame Scanner. A new model is proposed by combining Time Series Analysis with Fuzzy Support Vector Machine (TSA‐FSVM) for training, classification, and prediction of fuel types. With the help of 12,000 real‐time data collected from the Bharat Heavy Electricals Limited (BHEL), Tuticorin branch (a public sector company, which manufactures power plant equipment), a comparative analysis is performed using different Machine Learning algorithms with the proposed technique. From the results, it is found that TSA‐FSVM outperforms existing as well as other Machine Learning methods by increasing the accuracy of predicting the right fuel. Thus, it helps to avoid the boiler system explosion/OFF state, which leads to conserving the energy, increases electricity production, and cost‐efficiency.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.