Computational effort comparison of genetic algorithm and particle swarm optimization algorithms for the proportional–integral–derivative controller tuning of a pressurized water nuclear reactor

Mousakazemi, Seyed Mohammad Hossein

doi:10.1016/j.anucene.2019.107019

Cited by 38 publications

(12 citation statements)

References 21 publications

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“…In the literature, different cost functions were used such as integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE), integral time square error (ITSE), and others. 41,42 In addition, multi-objective functional optimization is used where multiple performance measures are optimized. 43 The cost functions used in this paper are listed in Table 2, where w is a positive constant representing the weight, and the error is defined as follows:…”

Section: Problem Formulationmentioning

confidence: 99%

Optimal controller tuning for P&O maximum power point tracking of PV systems using genetic and cuckoo search algorithms

Ahmed

Rahman

Alajmi

2020

Int Trans Electr Energ Syst

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Background: Tracking the maximum power point (MPPT) of photovoltaic (PV) systems is essential to increase the utilization efficiency. One of the most widely used algorithms is perturb and observe (P&O) technique.Methods: In this paper, tuning algorithms based on genetic algorithm (GA) and cuckoo search (CS) are proposed to provide optimal controller parameters for P&O MPPT. This optimal tuning is crucial in P&O MPPT applications of PV systems where it is difficult to tune the controllers using conventional tuning techniques to attain good performance because of the wide variations in the system parameters and its nonlinearity. The proposed tuning algorithms are used to overcome the drawbacks of the classical P&O MPPT under rapidly changing atmospheric conditions such as oscillation around the maximum power point (MPP) and low convergence speed.Results: The robustness of the algorithms is confirmed against fast varying insolation proving its ability to track the MPP in case of random and fast changing atmospheric conditions. An experimental set up of a 1 kW PV system is carried out and implemented using dSPACE DS1103 controller board and E4360 Keysight modular solar array emulator to validate the performance of the proposed optimal tuning algorithms. The measured results are in a close agreement with the simulation and the analytical system. Conclusion: Experimental and simulation results demonstrate a superior improvement in the transient and steady-state performances and in the maximum power tracking efficiency. The steady-state and the transient efficiencies of the presented work are 99.95% and 94.02% which are almost 17.59% and 10.61%, respectively superior to the classical P&O one and are 14.66% and

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Section: Problem Formulationmentioning

confidence: 99%

Optimal controller tuning for P&O maximum power point tracking of PV systems using genetic and cuckoo search algorithms

Ahmed

Rahman

Alajmi

2020

Int Trans Electr Energ Syst

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“…On the other hand, the numerical method -which is usually an optimization-based method -is a relative suitable solution for tuning FOPID for the PWR [18], [19]. Three optimization techniques is used in this study which are particle swarm optimization (PSO) [20], [21], gray wolf optimization (GWO) [22], [23] and ant lion optimization (ALO) [24], [25] and the evaluation will be based on the integral square error (ISE).…”

Section: Introductionmentioning

confidence: 99%

Output power control of nuclear reactor using ant lion optimization-based controller

Boghdady

Ibrahim

Aboul-Zahab

et al. 2022

IJEECS

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Power level control is a <span>critical issue in nuclear power stations due to its nonlinear dynamics. One of the most commonly used controllers is fractional order proportional–integral–derivative (FOPID). The FOPID is an enhanced and modern controlling system that has two additional added parameters. In this paper, comparison between particle swarm, gray wolf and ant lion optimization techniques is performed to determine the FOPID controller parameters. The nuclear reactor is a pressurized water reactor which is a fifth order nonlinear reactor model and is simulated using MATLAB software based on the point kinetic model. The integral square error (ISE) performance index is used to evaluate the performance of the three optimization techniques. The simulation results show that ant lion optimization for tuning the FOPID controller parameters gives the best performance and integral square error index better than the two other optimization techniques.</span>

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“…As the scale of the communication network becomes larger and larger, the number of network nodes is increasing, and the network business traffic is becoming more and more complex. e increasing demand poses a severe challenge to the load capacity of the network, and the problem of network congestion is becoming more and more serious [7][8][9]. Congestion control is of great significance for ensuring the robustness of network systems and maintaining high service quality.…”

Section: Introductionmentioning

confidence: 99%

Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

Zhou¹

2021

Mathematical Problems in Engineering

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Based on the closed-loop particle swarm feedback model, this paper proposes a graphical method to analyze the stability of the computer network dynamic balance system. First, based on the second-order time delay system model of congestion control, the stability of the system is described by characteristic pseudopolynomials. Secondly, based on the inverse line, the stability of the system is verified by graphical analysis methods, and the PID controller parameter range that guarantees the stability of the system is obtained, and the relationship between the controller proportional gain boundary and the network characteristic parameters is analyzed. Then, based on the analysis of the basic particle swarm optimization algorithm, the particle swarm evolution formula is divided into two parts, its own factors and social factors, and the influence of each part on the evolution speed and position of the particle swarm is analyzed, and an improved particle swarm is proposed. Finally, according to the above analysis, we find the corresponding equation from the appropriate solution in turn, thereby designing a class of particle swarm optimization algorithm with fewer intermediate variables. In view of the system involved in the classical PID control parameter tuning method, the improved particle swarm algorithm is applied to the parameter tuning and optimization of the PID controller. During the experiment, the improved PSO-PID controller optimization algorithm was used in the random early detection algorithm of active queue management, the process of the improved algorithm was researched and designed, and the relevant performance of the improved algorithm was verified through simulation experiments.

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Computational effort comparison of genetic algorithm and particle swarm optimization algorithms for the proportional–integral–derivative controller tuning of a pressurized water nuclear reactor

Cited by 38 publications

References 21 publications

Optimal controller tuning for P&O maximum power point tracking of PV systems using genetic and cuckoo search algorithms

Optimal controller tuning for P&O maximum power point tracking of PV systems using genetic and cuckoo search algorithms

Output power control of nuclear reactor using ant lion optimization-based controller

Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

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