Coordination of DG steam turbine plant automatic controller settings

The purpose of the paper is to develop a coordination methodology for the settings of automatic controllers of generator excitation and generator rotor speed of distributed generation steam turbine plants. The methods of digital signal processing under controller noise isolation and obtaining of spectral characteristics for complex transfer coefficient calculations are used to achieve the set purpose. A genetic algorithm is used as an optimization tool for the tuning coefficients of automatic excitation and speed controllers. The power supply system with a controlled distributed generation steam turbine has been simulated in the MATLAB software environment using Simulink, SimPowerSystems packages and a specialized software package for coordination of controller setting coefficients. The results of the study show that coordination of controller settings and application of predictive algorithms improve the damping properties of the system making frequency and voltage control faster and smoother. The conducted research and computer modeling allow to derive the following conclusions: a small constant inertia of the distributed generation plant generator rotors requires coordination of settings of automatic excitation and speed controllers; the use of an auto predictive automatic speed controller can significantly improve the damping properties of the regulator when controlling the turbogenerators of distributed generation plants connected to static and dynamic loads; the auto predictive automatic speed controller of the synchronous generator can be recommended to improve the efficiency of rotor speed regulation and controlling of steam turbine control valves. Practical use of the proposed algorithms for setting automatic excitation and speed controllers will improve the reliability of distributed generation plant operation and the quality of control processes.

distributed generation steam turbine, automatic voltage controllers, automatic speed controllers, coordination of settings, predictive algorithms, genetic algorithm