Abstract
Water level control is a crucial step for steam generators (SG) which are widely used to control the temperature of nuclear power plants. The control process is therefore a challenging task to improve the performance of water level control system. The performance assessment is another consideration to underline. In this paper, in order to get better control of water level, the nonlinear process was first expressed in terms of a transfer function (TF), a proportional-integral-derivative (PID) controller was then attached to the model. The parameters of the PID controller was finally optimized using particle swarm optimization (PSO). Simulation results indicate that the proposed approach can make an effective tracking of a given level set or reference trajectory.
Highlights
There is an increasing demand for energy
Steam is generated by the u-tube steam generator (UTSG), whose water level should be controlled in safe limits in order to maintain plant availability and economic feasibility of a nuclear power plant (NPP) [1]
The performance of optimized controller parameters obtained using particle swarm optimization (PSO) was observed on various levels and trajectories to observe the performance of the algorithm
Summary
There is an increasing demand for energy. Nuclear energy, which is one of the cleanest forms of energy, has increasing attraction and goverments invested funds to the develepment of nuclear energy. Complexity and nonlinearity of the plants, computational approaches were widely used and applied to real time systems for the modeling, prediction and optimization processes [11,12,13,14]. Since there has been an increasing demand to control the liquid level and flow, various control techniques were proposed to be applied to level control processes Among those techniques, PID controller receive many attention due to its robust performance, simplicity and its applicability to a large class of processes having different dynamics. All the given AI-based evolutionary computational techniques are supposed to determine the optimal set of controller parameters based on a given fitness function but the performance of of each method may significantly vary in different applications.
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