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Optimized Thermal Power Control for Nuclear Superheated-Steam Supply Systems Based on Multi-Layer Perception

机译：基于多层感知的核超热蒸汽供应系统优化的热功率控制

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摘要

Nuclear superheated-steam supply systems (Su-NSSS) produces superheated steam flow for electricity generation or process heat. Though the current Su-NSSS control law can guarantee satisfactory closed-loop stability, which regulates the nuclear power, primary coolant temperature and live steam temperature through adjusting the control rod speed as well as primary and secondary flowrates, however, the control performance needs to be further optimized. Motivated by the necessity of optimizing the thermal power response, a novel multi-layer perception (MLP) based model predictive control (MPC) is proposed in this paper. The thermal power of Su-NSSS is predicted by an MLP with online learning algo-rithm, and the control input is designed in the direction opposite to the gradient of a given performance index. Then, it is proved that this MLP-based MPC guarantees globally-bounded closed-loop stability. Finally, this newly-built MLP-based MPC for thermal power is implemented by forming a cascaded feedback control loop with the current Su-NSSS controller in the inner loop and this MPC in the outer loop. Numerical simulation results verify the correctness of theoretical result, and show the satisfactory improvement in optimizing the thermal power response.
机译：核过热蒸汽供应系统（SU-NSSS）为发电或工艺热量产生过热的蒸汽流量。虽然目前的SU-NSSS控制法可以保证令人满意的闭环稳定性，但通过调节控制杆速度以及初级和二次流量来调节核电，初级冷却剂温度和活蒸汽温度，但是控制性能需要进一步优化。通过优化热功率响应的必要性，本文提出了一种新的多层感知（MLP）模型预测控制（MPC）。通过在线学习算法的MLP预测SU-NSS的热功率，并且控制输入在与给定性能指标的梯度相反的方向上设计。然后，证明了基于MLP的MPC保证了全局有界闭环稳定性。最后，通过在内环中的当前SU-NSSS控制器中形成级联反馈控制回路和外环中的该MPC来实现这种基于MLP的基于MPC的热电MPC。数值模拟结果验证了理论结果的正确性，并显示了优化热功率响应的令人满意的改进。

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