Omar Mohamed

Predictive control of coal mills for improving supercritical power generation process dynamic responses

The paper is to study new control strategies for improvement of dynamic responses of a supercritical power generation process through an improved control to the associated fuel preparation performed by the coal milling process. Any control actions taking for the milling process will take a long time to show their influences onto the boiler, turbine and generator responses as the whole process experiences coal transmission, grinding, drying and blowing to the furnace. The control philosophy behind the work presented in the paper is to develop a control strategy to achieve prediction of the future demand for fuel input and implement control actions at the earliest possible time. The paper starts from description of the nonlinear mathematical model developed for the supercritical coal fired power plant and then moves onto control strategy development. Finally, the simulation study has been carried out to demonstrate the effect of the new predictive control.

Predictive control strategy for a supercritical power plant and study of influences of coal mills control on its dynamic responses

The paper is to investigate dynamic responses of supercritical power plants (SCPP) and study the potential strategies for improvement of their responses for Grid Code compliance. An approximate mathematical model that reflects the main features of SCPP is developed. The model unknown parameters are identified using Genetic Algorithms (GA) and the model is validated over a wide operating range. A model based predictive control (MPC) is then proposed to speed up the dynamic responses of the power plant by adjusting the reference of the plant local controls instead of direct control signal applications. Simulation results have shown encouraging improvement in performance of the plant with no interference with its associated local controllers.

Mathematical Modelling for Coal Fired Supercritical Power Plants and Model Parameter Identification Using Genetic Algorithms

The paper presents the progress of our study of the whole process mathematical model for a supercritical coal-fired power plant. The modelling procedure is rooted from thermodynamic and engineering principles with reference to the previously published literatures. Model unknown parameters are identified using Genetic Algorithms (GAs) with 600MW supercritical power plant on-site measurement data. The identified parameters are verified with different sets of measured plant data. Although some assumptions are made in the modelling process to simplify the model structure at a certain level, the supercritical coal-fired power plant model reported in the paper can represent the main features of the real plant once-through unit operation and the simulation results show that the main variation trends of the process have good agreement with the measured dynamic responses from the power plants.

Comparative study between subspace method and prediction error method for identification of gas turbine power plant

This paper aims to introduce generalized statespace models of power plant gas turbine system. Once verified, those models can be used in system real-time monitoring and control of power plants. The models are of generalized statespace structure and their parameters are optimized from normal operating records first using subspace identification method. Then those results are compared with the identified responses using prediction error method. Simulation results have shown corresponding advantages of simplicity and following the main dynamic variations of the system for the proposed models with some essence differences between the two methods. Also, residual analysis has been run to confirm the models validity.

The application of System Identification via Canonical Variate Algorithm to North Benghazi gas turbine Power generation system

The topic of modeling and identification of gas turbines has become an interesting research area for many years and will become so for many years to come. This paper clarifies what is known as Canonical Variate Algorithm or canonical variate analysis (CVA) method of subspace state space system identification. A gas turbine operating currently in North Benghazi Power Plant (NBPP) is the process chosen to be our focus of study in the paper. The CVA is described from mathematics and linear algebra view points. The process of gas turbine under investigation is illustrated and discussed. Through gathered operating data from the power plant under study and MATLAB System Identification Toolbox, the state space model is developed and tested against different data signals. Simulation results have shown the robustness and the accuracy of the presented method of identification.

D-Q model and control of a three-phase induction motor considering mutual flux saturation effect

The field oriented control of the three-phase induction motor (IM) has become an increasingly popular control methodology, where control of the flux linkage can be effectively decoupled from the torque, allowing for segregated flux and speed controls of the IM. However, if flux saturation effect was ignored, reductions in the reliability of the control of the IM and degradation in performance are expected. In this paper, the MATLAB SIMULINK d-q model of the three — phase IM is analyzed, and a d-q control schematic of the IM is applied. Results of torque and speed responses to the reference inputs were obtained in two separate cases; the first one supposes that the stator current is linearly proportional to stator voltage at all times (no flux saturation exists), and the second case, which is more practical, involves the mutual flux saturation. A comparison between the two cases in terms of torque and speed performances of the IM is yielded.