Design And Development Of Energy-Saving Control System For Coal-Fired Boiler

System reform of electric power in our country started in2002, the power plant and thepower grid were separated, and the state grid buys electricity from the power plant whoseprice of electricity is lower. So power plants in order to reduce power generation costs andimprove their own competitiveness, need to adopt effective energy saving measures. Forindustrial process energy saving means mainly include hardware, software, and managementof energy saving. According to the specific condition of weifang yaxing thermal power plant,this paper explores in the following aspects, and has obtained the expected effect.Hardware for energy conservation, adopting the xinhua XDPS-400+distributed controlsystem for upgrading the original DCS system in power plant, adding necessary data pointsand control actuators to the control system. For induced draft fan, adopts high voltagefrequency conversion system instead of the original damper baffle to control air volume andpressure in the flue, simulates and analyzes the performance of the high voltage converterwith MATLAB. On the base of the new DCS system, increase coal-fired boiler combustionoptimization systems, and it receives data of boiler operation from DCS system. Aftermodeling and optimizing output optimization control, DCS system receives optimal controland adjusts boiler combustion to make the combustion in the optimal state.Software for energy saving, in this paper, based on the analysis of steam drum waterlevel three impulse control, we adopt the adaptive PID fuzzy control to control the steamdrum water level. For boiler combustion optimization systems, boiler operation data can bedivided into the training sample data and the test sample data, and we preprocess the data.Respectively, use the training sample data to build LSSVM model, BP neural network modeland RBF neural network model of oxygen content in flue gas in the process of coal-fired boiler combustion, and use the test sample data to test boiler combustion model accuracy forsoft measurement, and take field test for the soft measure model. Field test results show thatsoft measurement relative error of the model is within5%, conforming to the requirement ofthe measurement accuracy. We use the genetic algorithm for boiler combustion optimizationand control the amount of the boiler oxygen content in flue gas in set value to improve theefficiency of boiler combustion. We take field test for optimal control and the test resultsshow that the optimization algorithm of saving coal rate was1.2%-2.0%.Power plant makes the retrofit in view of the above points, the boiler combustionperformance has greatly improved than before, but some problems remains to be furthersolved.