Equilibrium optimizer tuned novel
            <scp>FOPID‐DN</scp>
            controller for automatic voltage regulator system

Paliwal, Nikhil; Srivastava, Laxmi; Pandit, Manjaree

doi:10.1002/2050-7038.12930

Cited by 28 publications

(12 citation statements)

References 97 publications

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“…To derive the constraints that can ensure the stability of the generated family of plants from the perturbed polynomial 'P', we substitute λ = jω in Equation ( 16) and assume η = ω 2 then the perturbed polynomial 'P' can be defined in Equation (17),…”

Section: Robust Mpc Formulationmentioning

confidence: 99%

“…In [15,16], Jaya optimization algorithm (JOA) and gradient-based optimization (GBO) algorithm are introduced for the tuning of a fractional-order PID controller for AVR. In addition, a fractional-order PID including derivative with filter factor is adjusted based on equilibrium optimizer (EO) for AVR in [17]. In [18,19], a fractional PID controller for an AVR system is designed using particle swarm optimization (PSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

et al. 2021

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This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

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Section: Robust Mpc Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

et al. 2021

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“…As is the case for the DC motor, a controller is also required for efficient operation of an AVR system. In that respect, several controller structures such as PID controller (Bingul and Karahan, 2018; Zhou et al, 2019), FOPID controller (Bhookya and Jatoth, 2019; Bhullar et al, 2020b), PID plus second order derivative (PIDD 2 ) controller (Mokeddem and Mirjalili, 2020; Mosaad et al, 2018) and derivatives of PID controller (Moschos and Parisses, in press; Paliwal et al, 2021) along with neural network predictive control (Elsisi, 2019) and state feedback controller (Eke et al, 2021; Gozde, 2020; Mary et al, 2021) have so far been utilized. Similar to the case in DC motors, the PID controller is the most preferred controller for an AVR system, as well (Ayas, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

İzci

Ekinci

Zeynelgil

et al. 2021

Transactions of the Institute of Measurement and Control

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This study deals with the controlling the speed of a direct current (DC) motor via a fractional order proportional–integral–derivative (FOPID) controller and maintaining the terminal voltage level of an automatic voltage regulator (AVR) via a proportional–integral–derivative plus second order derivative (PIDD2) controller. To adjust the parameters of those controllers, a novel improved slime mould algorithm (ISMA) is proposed. The latter is a novel metaheuristic algorithm developed in this work. The proposed algorithm aims to improve the original SMA in terms of exploration with the aid of a modified opposition-based learning scheme and in terms of exploitation with the aid of the Nelder–Mead simplex search method. A time domain objective function, which includes time response specifications of steady state error and maximum overshoot along with rise and settling times, is used as a performance index to design the FOPID controller-based DC motor system and PIDD2 controller-based AVR system. The performance of the proposed novel approaches for both systems are assessed through time and frequency domain simulations along with statistical tests which show the greater performance of the improved algorithm. Further to this, the efficacy of the proposed approaches for both systems is compared with other available and effective approaches in the literature. The extensive comparative results demonstrate the proposed method to be superior to those state-of-the-art approaches for both DC motor speed and AVR control systems.

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“…Driven by the need for maintaining the terminal voltage stability in power systems, different adaptive control approaches [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], such as neural network methods [11] and optimization approaches [12] of AVRs, are being explored for terminal voltage stability purposes. An efficient, robust, and adaptive AVR control is necessary due to uncertainty, nonlinearity, and high-level penetration of distributed energy resources in modern power grids.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…e drawback of the work is that the proposed method is only tested under changes in some AVR system parameters and did not reflect the robustness of the AVR control system under fault or disturbance conditions or from the instability point of view. Papers [17,18] make use of the fractionalorder PID in addition to the second-order derivative controller (FOPIDD) to achieve a better transient response at a terminal voltage of AVR. In [17], the parameters of the controller are tuned optimally by the Equilibrium Optimizer (EO) algorithm, whereas [18] utilizes a multiverse optimizer (MVO) algorithm to tune the parameters of the controller.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Modified Q-Learning Method for Automatic Voltage Regulation in Wide-Area Multigeneration Systems

Abegaz

Zarrabian

2022

International Transactions on Electrical Energy Systems

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The state-estimation and optimal control of multigeneration systems are challenging for wide-area systems having numerous distributed automatic voltage regulators (AVR). This paper proposes a modified Q-learning method and algorithm that aim to improve the convergence of the approach and enhance the dynamic response and stability of the terminal voltage of multiple generators in the experimental Western System Coordinating Council (WSCC) and large-scale IEEE 39-bus test systems. The large-scale experimental testbed consists of a six-area, 39-bus system having ten generators that are connected to ten AVRs. The implementation shows promising results in providing stable terminal voltage profiles and other system parameters across a wide range of AVR systems under different test scenarios including N-1 contingency and fault conditions. The approach could provide significant stability improvement for wide-area systems as compared to the implementation of conventional methods such as using standalone AVR and/or power system stabilizers (PSS) for the wide-area control of power systems.

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Equilibrium optimizer tuned novel FOPID‐DN controller for automatic voltage regulator system

Cited by 28 publications

References 97 publications

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

Modified Q-Learning Method for Automatic Voltage Regulation in Wide-Area Multigeneration Systems

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