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- 저자 : Seyed Mohammad Hossein Mousakazemi (검색결과 3 건)
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Seyed Mohammad Hossein Mousakazemi,Navid Ayoobian,GHOLAM REZA ANSARIFAR 한국원자력학회 2018 Nuclear Engineering and Technology Vol.50 No.6
Various controllers such as proportionaleintegralederivative (PID) controllers have been designed andoptimized for load-following issues in nuclear reactors. To achieve high performance, gain tuning is ofgreat importance in PID controllers. In this work, gains of a PID controller are optimized for power-levelcontrol of a typical pressurized water reactor using particle swarm optimization (PSO) algorithm. Thepoint kinetic is used as a reactor power model. In PSO, the objective (cost) function defined by decisionvariables including overshoot, settling time, and stabilization time (stability condition) must be minimized(optimized). Stability condition is guaranteed by Lyapunov synthesis. The simulation resultsdemonstrated good stability and high performance of the closed-loop PSOePID controller to responsepower demand
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Mousakazemi, Seyed Mohammad Hossein Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.8
Metaheuristic algorithms can work well in solving or optimizing problems, especially those that require approximation or do not have a good analytical solution. Particle swarm optimization (PSO) is one of these algorithms. The response quality of these algorithms depends on the objective function and its regulated parameters. The nonlinear nature of the pressurized light-water nuclear reactor (PWR) dynamics is a significant target for PSO. The two-point kinetics model of this type of reactor is used because of fission products properties. The proportional-integral-derivative (PID) controller is intended to control the power level of the PWR at a short-time transient. The absolute error (IAE), integral of square error (ISE), integral of time-absolute error (ITAE), and integral of time-square error (ITSE) objective functions have been used as performance indexes to tune the PID gains with PSO. The optimization results with each of them are evaluated with the number of function evaluations (NFE). All performance indexes achieve good results with differences in the rate of over/under-shoot or convergence rate of the cost function, in the desired time domain.
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Henry gas solubility optimization for control of a nuclear reactor: A case study
Seyed Mohammad Hossein Mousakazemi 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.3
Meta-heuristic algorithms have found their place in optimization problems. Henry gas solubility optimization(HGSO) is one of the newest population-based algorithms. This algorithm is inspired by Henry'slaw of physics. To evaluate the performance of a new algorithm, it must be used in various problems. Onthe other hand, the optimization of the proportionaleintegralederivative (PID) gains for load-followingof a nuclear power plant (NPP) is a good challenge to assess the performance of HGSO. Accordingly, thepower control of a pressurized water reactor (PWR) is targeted, based on the point kinetics model withsix groups of delayed-neutron precursors. In any optimization problem based on meta-heuristic algorithms,an efficient objective function is required. Therefore, the integral of the time-weighted squareerror (ITSE) performance index is utilized as the objective (cost) function of HGSO, which is constrainedby a stability criterion in steady-state operations. A Lyapunov approach guarantees this stability. Theresults show that this method provides superior results compared to an empirically tuned PID controllerwith the least error. It also achieves good accuracy compared to an established GA-tuned PID controller.
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