Research On The Internal Model Control Of PWR Steam Generator’s Water Level Control Based On Multiple Models

Steam generator is a part of the power plant in PWR. The SG water level should be controlled within a certain range during the operation. Either lower or higher water level damages the nuclear power plant’s normal operation. However, due to the variable conditions, parameter uncertainty and non-linearity related to the SG, it’s often difficult to obtain satisfactory control result, when we adopt a precise mathematical model. Even worse, it may cause an uncontrollable result. Therefore, in order to achieve an effective SG water level controlling goal, taking the SG characteristics into account, this paper applies the internal model control(IMC) based on the multiple models strategy.The IMC has several advantages, such as the effectiveness to the delaying-system, its immunity, its simple structure, easy to adjust and online parameter tuning. Firstly, we use a second-order equivalent model to design the SG water level controller, as the sub-controller, in several typical working conditions. Then, based on the performance indicators, we use the weighted strategy, online calculating the weighting coefficients. After that, we define the sum of each controller output and its weight’s product as the total output signal of the actual controller. Multi-model internal model control strategy has good robustness、convenient computing characteristic. And it can overcome the output disturbance, because of the changes of object properties. The simulation result proves that such controller can bring good stability and good speed tracking performance, and can effectively overcome the continuously variable load impact putting on the water level.As for the optimal weighting coefficient’s searching method, this paper proposed a novel particle swarm algorithm, which is an offline optimization program. In this way, we can get the optimal weighting coefficient values according to the varying working conditions. The simulation result shows that, comparing to the online algorithm in a certain working condition, the offline particle swarm optimization algorithm indeed has a significant advantage.