Abstract
Although there have been some severe nuclear accidents such as Three Mile Island (USA), Chernobyl (Ukraine) and Fukushima (Japan), nuclear fission energy is still a source of clean energy that can substitute for fossil fuels in a centralized way and in a great amount with commercial availability and economic competitiveness. Since the pressurized water reactor (PWR) is the most widely used nuclear fission reactor, its safe, stable and efficient operation is meaningful to the current rebirth of the nuclear fission energy industry. Power-level regulation is an important technique which can deeply affect the operation stability and efficiency of PWRs. Compared with the classical power-level controllers, the advanced power-level regulators could strengthen both the closed-loop stability and control performance by feeding back the internal state-variables. However, not all of the internal state variables of a PWR can be obtained directly by measurements. To implement advanced PWR power-level control law, it is necessary to develop a state-observer to reconstruct the unmeasurable state-variables. Since a PWR is naturally a complex nonlinear system with parameters varying with power-level, fuel burnup, xenon isotope production, control rod worth and etc., it is meaningful to design a nonlinear observer for the PWR with adaptability to system uncertainties. Due to this and the strong learning capability of the multi-layer perceptron (MLP) neural network, an MLP-based nonlinear adaptive observer is given for PWRs. Based upon Lyapunov stability theory, it is proved theoretically that this newly-built observer can provide bounded and convergent state-observation. This observer is then applied to the state-observation of a special PWR, i.e., the nuclear heating reactor (NHR), and numerical simulation results not only verify its feasibility but also give the relationship between the observation performance and observer parameters.
Highlights
The growing requirements for electricity and the pollution caused by burning fossil fuels has led to a renaissance of nuclear energy industry, even if there have been some severe accidents such as ThreeMile Island (USA), Chernobyl (Ukraine) and Fukushima (Japan)
To verify the feasibility of this newly-built neural observer, it is applied to the state-observation of a nuclear heating reactor (NHR) which is a small pressurized water reactor (PWR) developed by Institute of Nuclear and New Energy Technology (INET) at Tsinghua University
Since NHR dynamics has both strong nonlinearity and high uncertainty, in order to implement advanced power-level controllers for higher operation performance, it is very meaningful to realize the adaptive state-observation for the NHR
Summary
The growing requirements for electricity and the pollution caused by burning fossil fuels has led to a renaissance of nuclear energy industry, even if there have been some severe accidents such as ThreeMile Island (USA), Chernobyl (Ukraine) and Fukushima (Japan). Since power-level control is a quite crucial technique which guarantees operation stability and efficiency for nuclear reactors, developing high performance power-level regulators is quite meaningful for the current rebirth of nuclear energy industry. In order to implement the advanced power-level control strategies for stronger dynamic performance, some observation structure should be used to reconstruct the state-variables that cannot be obtained directly through measurement. In this case, the simpler solution is to utilize the linear observers such as the Luenberger observer [1] and Kalman filter [2,3].
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