A Neural-Network-Based Nonlinear Adaptive State-Observer for Pressurized Water Reactors

Abstract

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. OPEN ACCESSEnergies 2013, 6 5383

MLP compensated pd power-level control for modulator high temperature gas-cooled reactors

“…where z is given by (28) and (29), z , S,Ŝ andˆ S is defined by (10), (11), (20) and (21) respectively, activation function s is chosen as the hyperbolic tangent function given by (12), feedback gain k NP is a positive scalar which satisfies (63), both feedback gains k TP and k TD are positive scalars chosen experientially, both W and V are given positive scalars, and both W and V are given positive-definite matrices.…”

“…Arab-Alibeik proposed a multilayer perception (MLP) based inverse power-level control for the PWRs [10]. Dong gave an MLP-based nonlinear state-observer which provides the convergent and bounded state-observation for those PWR-like nuclear reactors [11].…”

Comparing the Substitution of Nuclear Energy or Renewable Energy for Fossil Fuels between the United States and Africa

“…Previous studies on global warming and abnormal climate phenomena found that the energy used for electricity generation results in carbon dioxide (CO 2 ) emissions and greenhouse effects [8][9][10]. To slow down CO 2 emissions and generate cleaner electricity, previous studies have suggested the usage of clean energy to replace fossil fuels [11][12][13]. Previous studies have regarded nuclear energy or renewable energy as clean energy that can be used to generate electricity and have proposed using clean energy to replace fossil fuels.…”

Input-Output Feedback Linearization Control for a PWR Nuclear Power Plant

“…Neural networks (NNs) have an inherent ability to learn and approximate nonlinear systems with an arbitrary accuracy [15]. Some kinds of feedforward NNs provided suitable and improved modeling performance in PWR systems [16]- [18]. However, the main drawback of feedforward NNs is that they are essentially static and limited in representing complex systems.…”