Kalman Filter Based State Estimation of a Thermal Power Plant

Tangentially-fired furnaces (TFF) are vortex-combustion units and are widely used in steam generators of thermal power plants. Perfect modeling and simulation of furnace gas temperature is quite difficult, due to its complex aerodynamics of burning particles, flame stability and hot gas flow distribution throughout the furnace. The temperature of the furnace gas depends on many parameters such as the inclination angle (tilt angle), fuel quality, burn out percentage and the flow rates in the burners for each of the furnace corners. However, the measurements are not available in the existing furnace operated at Neyveli Lignite Corporation (NLC), Neyveli. Thus, state estimation of temperature is an important prerequisite for safe and economical process operations. It is an integral part of applications such as process monitoring, fault detection and diagnosis, process optimization, and model-based control. Because all the process variables are generally not measured, an observer can be designed to generate an estimate of the state by making use of the relevant process inputs, outputs, and process knowledge, in the form of a mathematical model. The aim is to design a good state estimator for the furnace. Linear Kalman Filter (LKF) and Extended Kalman Filter (EKF) algorithms are developed for this problem and simulation results are compared.