Control of Air Pressurizer Levels on Pressurized Water Reactor (PWR) with Fractional Order PID Control System

Damayanti, Rissa; Halim, Abdul; Bakhri, Syaiful

doi:10.17977/um024v5i22020p071

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…However, systems that are real-time may be realistically represented by expanding the controller's degrees of freedom, where the fractional-order (FO) operator can be used in lieu of the I and D control actions' integer-order equivalents. The FOPI, FOPD, and FOPID were used in nonlinear schemes as reliable controllers due to their enhanced degree of freedom [20,21].…”

Section: B) Literature Review and Research Gapmentioning

confidence: 99%

Optimal controller design for reactor core power stabilization in a pressurized water reactor: Applications of gold rush algorithm

Abdelfattah,

Esmail,

kotb

et al. 2024

PLoS ONE

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Nuclear energy (NE) is seen as a reliable choice for ensuring the security of the world’s energy supply, and it has only lately begun to be advocated as a strategy for reducing climate change in order to meet low-carbon energy transition goals. To achieve flexible operation across a wide operating range when it participates in peak regulation in the power systems, the pressurised water reactor (PWR) NE systems must overcome the nonlinearity problem induced by the substantial variation. In light of this viewpoint, the objective of this work is to evaluate the reactor core (main component) of the NE system via different recent optimization techniques. The PWR, which is the most common form, is the reactor under investigation. For controlling the movement of control rods that correspond with reactivity for power regulation the PWR, PID controller is employed. This study presents a dynamic model of the PWR, which includes the reactor core, the upper and lower plenums, and the piping that connects the reactor core to the steam alternator is analyzed and investigated. The PWR dynamic model is controlled by a PID controller optimized by the gold rush optimizer (GRO) built on the integration of the time-weighted square error performance indicator. Additionally, to exhibit the efficacy of the presented GRO, the dragonfly approach, Arithmetic algorithm, and planet optimization algorithm are used to adjust the PID controller parameters. Furthermore, a comparison among the optimized PID gains with the applied algorithms shows great accuracy, efficacy, and effectiveness of the proposed GRO. MATLAB\ Simulink program is used to model and simulate the system components and the applied algorithms. The simulation findings demonstrate that the suggested optimized PID control strategy has superior efficiency and resilience in terms of less overshoot and settling time.

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Section: B) Literature Review and Research Gapmentioning

confidence: 99%

Optimal controller design for reactor core power stabilization in a pressurized water reactor: Applications of gold rush algorithm

Abdelfattah,

Esmail,

kotb

et al. 2024

PLoS ONE

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show abstract

“…Different methods of PID gains regulation have been advanced in controlling nuclear power plants, like genetic algorithm (GA) [8]. Halim and Bakhri [9] aimed to obtain a safety control system using the fractional-order PID (FOPID) control system and prove that using the control system will provide better results than other existing system applications based-PID controllers. Also, with these good results, it is difficult to control nuclear power plant systems using these classical controllers as of those complex, time-varying and parameters that are not well-known.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Self Tuned-PID Controller for Stabilization of Core Power in a Pressurized Water Reactor

Abdelfattah

Kotb

Esmail

et al. 2022

IJRCS

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There has been a lot of interest in generating electricity using nuclear energy recently. This interest is due to the features of such a source of energy. The main part of the nuclear energy system is the reactor core, especially the most widely used Pressurized Water Reactor (PWR). This reactor is the hottest part of the nuclear system; security risks and economic possibilities must be considered. Controlling this reactor can increase the security and efficiency of nuclear power systems. This study presents a dynamic model of the (PWR), including the reactor's core, the plenums of the upper and lower, and the connecting piping between the reactor core and steam generator. In addition, an adaptive neuro-fuzzy (ANFIS) self-tuning PID Controller for the nuclear core reactor is presented. This adaptive controller is used to enhance the performance characteristics of PWR by supporting the profile of the reactor power, the coolant fuel, and hot leg temperatures. The suggested proposed ANFIS self-tuning controller is estimated through a comparison with the conventional PID, neural network, and fuzzy self-tuning controllers. The results showed that the proposed controller is best over traditional PID, neural network, and fuzzy self-tuning controllers. All simulations are throughout by using MATLAB/SIMULINK.

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“…A fuzzy PID controller is adopted by Sheta et al [15] for VVER 1200 pressurizer. The fractional order PID controller is designed by Damayanti et al [16] for the PWR pressurizer level controller which is different from pressure control logic. Fractional order neural transient modeling of the primary circuit of ACP1000 based nuclear power plant is carried out by Malik et al [17] in LabVIEW for a generation-3 nuclear power plant.…”

Section: Introductionmentioning

confidence: 99%

Fractional Order Modeling of PWR Pressurizer Dynamics and Fractional Order Nonlinear H∞ Controllers Design in LabVIEW

Malik

Memon

Arshad

et al. 2022

PPASA

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The novel fractional order intelligent transient dynamics and advanced fractional order nonlinear robust control synthesis scheme of the Pressurized Water Reactor (PWR) pressurizer are addressed in this research work. The Graphical User Interface (GUI) is designed for closed-loop model-based PWR pressurizer dynamical studies in LabVIEW. Based on the demand for power, the reactor power and turbine power are predicted using a fractional order backpropagation algorithm in an open loop configuration. Using turbine power and heater power as input variables, pressurizer level, pressurizer pressure and coolant average temperature as output variables, the open loop multi-input multi-output (MIMO) dynamic model of pressurizer is estimated using fractional order artificial intelligence in LabVIEW. Four fractional order robust nonlinear H∞ sub-controllers are designed for charging flow, spray flow, proportional heaters power and backup heaters power. All the dynamic controller models are in fractional order nonlinear H∞ framework and are designed in LabVIEW. The performance of the proposed design work is evaluated in closed loop configuration at 100 %, 75 % and 15 % in steady state conditions. Dynamic transient analysis is performed from 100% to 90% power reduction scenario and found satisfactory and within design limits and robust bounds.

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Control of Air Pressurizer Levels on Pressurized Water Reactor (PWR) with Fractional Order PID Control System

Cited by 4 publications

References 14 publications

Optimal controller design for reactor core power stabilization in a pressurized water reactor: Applications of gold rush algorithm

Optimal controller design for reactor core power stabilization in a pressurized water reactor: Applications of gold rush algorithm

Adaptive Neuro-Fuzzy Self Tuned-PID Controller for Stabilization of Core Power in a Pressurized Water Reactor

Fractional Order Modeling of PWR Pressurizer Dynamics and Fractional Order Nonlinear H∞ Controllers Design in LabVIEW

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