Advanced Control Structures of Turbo Generator System of Nuclear Power Plant

Abstract

In the paper a synthesis of advanced control structures of turbine and synchronous generator for nuclear power plant working under changing operating conditions (supplied power level) is presented. It is based on the nonlinear models of the steam turbine and synchronous generator cooperating with the power system. The considered control structure consists of multi-regional fuzzy control systems with local linear controllers, including PID controllers, in particular control loops of turbine and generator. Soft switching between the local controllers is performed by the Takagi-Sugeno-Kang fuzzy logic mechanism. Parameters of the local controllers were optimally tuned for a priori chosen operating points within the range of 50-100% of nominal active power generated by the synchronous generator. Simulation results show that proposed advanced control structure is superior to widely used classic control structure (local controllers tuned for nominal operating points). During simulations the data of the real devices was used, respectively for the 4 CK 465 steam turbine and synchronous generator GTHW-600, which were planned to be used in the first Polish nuclear power plant in Żarnowiec.

Power System Stabilizer as a Part of a Generator MPC Adaptive Predictive Control System

“…The paper is a continuation of research on predictive control in a power plant. Previous work concerned the analysis of turbine and generator control systems using fuzzy logic [34,35], gain scheduling [36], MPC control [37], and DMPC (Distributed MPC) predictive control [33]. The presented results are an extension of previous works by a detailed analysis of the behavior of the generator controller and an additional system stabilizer module proposed as a single model predictive controller with an additional input.…”

“…The selection method of controllers' parameters is briefly presented in Figure 10. In order to obtain the optimal values for the gains of the controller, namely K p , K i , K d , T i , T d , and T 1 , T 2 for a simple power system stabilizer, the gradient algorithm has been run over a 100 of iterations with a random initial guess [35]. In the case of the MPC controller, the control horizon was set to s = 1 sample as the plant is non-linear, and the model is changing online with the simulation.…”

Numbers, Please: Power- and Voltage-Related Indices in Control of a Turbine-Generator Set

“…The proposed solution was designed for control systems synthesis and their quality assessment and is not designed to replace existing indices used to assess compliance with the regulations. As being control oriented, these indices were used to design a set of controllers of a turbine-generator set in a nuclear power plant: a QDMC model predictive controller [38], a distributed model predictive controller [30], a fuzzy controller [39] and a controller using gain scheduling [40]. They were used to tune the control systems, i.e., to optimize the parameters of controllers, and to compare the quality of several different solutions.…”