Application of Static State Estimator in Boilers of Thermal Power Stations

Abstract

Controlling bad data is one of the most important challenges in power systems because it affects the continuity of plants operation in an appropriate way. Bad data can appear morely in the control center of thermal power plants due to the malfunctioning of sensors which feeding the central processing unit (CPU) through wireless or cables communication system. The happened malfunctioning is because of the harsh environment especially in the boiler sections where an elevated temperature results due to fuel combustion. The bad data may cause inaccurate decisions from the central unit which may lead to sudden shutdowns of the overall station. The sudden shutdown results in losses in the generation of electrical energy where restarting the station requires long time to reach steady state operation. The bad data can be treated by an estimator based on suitable mathematical model. In this paper an estimator is presented to treat the bad data received from the boiler of the thermal power stations. The mathematical model of the estimator is written to simulate the boiler system based on heat transfer principles. The weighted least squares algorithm technique (WLS) is selected to execute the state estimator. The effectiveness of the estimator is tested by inserting white noise and bad data. The results show that the estimator is effective where it can identify bad data. The presented estimator is applied on the boiler of Al-Doura in Baghdad thermal station as a case study, due to the low performance of metering system of this plant. The results show that the estimator can detect and identify even to 10 bad data in the same time at a redundancy ratio of 2.125.