Hybrid Intelligent Warning System for Boiler tube Leak Trips

Abstract

Abstract. Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak trips have been proposed. The first intelligent warning system (IWS-1) represents the use of pure artificial neural network system whereas the second intelligent warning system (IWS-2) represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. Genetic algorithm (GA) was adopted in IWS-2 to optimize the ANN topology and the boiler parameters. An integrated data preparation framework was established for 3 real cases of boiler tube leak trip based on a thermal power plant in Malaysia. Both the IWSs were developed using MATLAB coding for training and validation. The hybrid IWS-2 performed better than IWS-1.The developed system was validated to be able to predict trips before the plant monitoring system. The proposed artificial intelligent system could be adopted as a reliable monitoring system of the thermal power plant boilers.

Artificial Neural Network and Its Applications in the Energy Sector – An Overview

“…Timely discovery and diagnosis of boiler trips have been prescribed for continued safe operation and as an antidote to unnecessary hike in the operating cost of thermal power plants. This assertion formed the basis of the research on an intelligent cautionary scheme for boiler tube leak trips conducted by Singh et al (2017). This research which focused on obtaining an intelligent scheme capable of optimal prediction of boiler trips considered three actual cases of thermal power plant boiler tube leak trip.…”